Muscle-bound primordial stem cells give rise to myofiber-associated myogenic and non-myogenic progenitors

Adipogenic Differentiation of Muscle Derived Cells is Repressed by Inhibition of GSK-3 Activity

Intramuscular fat is important in large animal livestock species in regard to meat quality and in humans is of clinical significance in particular in relation to insulin resistance. The canonical Wnt signalling pathway has been implicated at a whole body level in regulating relative levels of adiposity versus lean body mass. Previously we have shown that pig muscle cells can undergo adipogenic differentiation to a degree that is dependent upon the specific muscle source. In this work we examine the role of the canonical Wnt pathway which acts through inactivation of glycogen synthase kinase-3 (GSK-3) in the regulation of adipogenic differentiation in muscle cells derived from the pig semimembranosus muscle.

The application of lithium chloride to muscle derived cells significantly increased the phosphorylation of GSK-3β and thus inhibited its activity thus mimicking Wnt signaling. This was associated with a significant decrease in the expression of the adipogenic transcription factor PPARγ and an almost complete inhibition of adipogenesis in the cells. The data also suggest that GSK-3α plays, at most, a small role in this process.

Studies in vivo have suggested that the Wnt pathway is a major regulator of whole body adiposity. In this study we have shown that the ability of cells derived from porcine skeletal muscle to differentiate along an adipogenic lineage, in vitro, is severely impaired by mimicking the action of this pathway. This was done by inactivation of GSK-3β by the use of Lithium Chloride.

A generic, cost-effective, and scalable cell lineage analysis platform

Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing-based methods for cell lineage analysis depend on low-resolution bulk analysis or rely on extensive single-cell sequencing, which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective, and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data, and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way toward large-scale human cell lineage discovery.

Accuracy of Answers to Cell Lineage Questions Depends on Single-Cell Genomics Data Quality and Quantity

Advances in single-cell (SC) genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells, as determined by phylogenetic analysis of the somatic mutations harbored by each cell. Theoretically, complete and accurate knowledge of the genome of each cell of an individual can produce an extremely accurate cell lineage tree of that individual. However, the reality of SC genomics is that such complete and accurate knowledge would be wanting, in quality and in quantity, for the foreseeable future. In this paper we offer a framework for systematically exploring the feasibility of answering cell lineage questions based on SC somatic mutational analysis, as a function of SC genomics data quality and quantity. We take into consideration the current limitations of SC genomics in terms of mutation data quality, most notably amplification bias and allele dropouts (ADO), as well as cost, which puts practical limits on mutation data quantity obtained from each cell as well as on cell sample density. We do so by generating in silico cell lineage trees using a dedicated formal language, eSTG, and show how the ability to answer correctly a cell lineage question depends on the quality and quantity of the SC mutation data. The presented framework can serve as a baseline for the potential of current SC genomics to unravel cell lineage dynamics, as well as the potential contributions of future advancement, both biochemical and computational, for the task.

A human cell lineage tree describes the entire developmental dynamics of a person starting from the zygote and ending with each and every extant cell. Fundamental open problems in biology and medicine are in fact questions about the human cell lineage tree: its structure and its dynamics in development, growth, renewal, aging, and disease. Consequently, a method to know the human cell lineage tree would allow resolving these problems and enable a leapfrog advance in human knowledge and health. Recent advancements in single-cell genomics have the potential to uncover various properties of the human cell lineage tree and thus promote our understanding of various biological phenomena. In this paper we present a computational framework along with specific results, which enable to understand what can be achieved using the limitations of current technologies and predict future capabilities based on future improvements. This approach can serve as a valuable tool for researchers who plan to perform lineage experiments both in designing and optimizing the actual experimental needs and predicting the costs and limitations of the plan. This work can also help researchers focus on developing what is needed for future advancements.

Lineage grammars: describing, simulating and analyzing population dynamics

Background

Precise description of the dynamics of biological processes would enable the mathematical analysis and computational simulation of complex biological phenomena. Languages such as Chemical Reaction Networks and Process Algebras cater for the detailed description of interactions among individuals and for the simulation and analysis of ensuing behaviors of populations. However, often knowledge of such interactions is lacking or not available. Yet complete oblivion to the environment would make the description of any biological process vacuous. Here we present a language for describing population dynamics that abstracts away detailed interaction among individuals, yet captures in broad terms the effect of the changing environment, based on environment-dependent Stochastic Tree Grammars (eSTG). It is comprised of a set of stochastic tree grammar transition rules, which are context-free and as such abstract away specific interactions among individuals. Transition rule probabilities and rates, however, can depend on global parameters such as population size, generation count, and elapsed time.

Results

We show that eSTGs conveniently describe population dynamics at multiple levels including cellular dynamics, tissue development and niches of organisms. Notably, we show the utilization of eSTG for cases in which the dynamics is regulated by environmental factors, which affect the fate and rate of decisions of the different species. eSTGs are lineage grammars, in the sense that execution of an eSTG program generates the corresponding lineage trees, which can be used to analyze the evolutionary and developmental history of the biological system under investigation. These lineage trees contain a representation of the entire events history of the system, including the dynamics that led to the existing as well as to the extinct individuals.

Conclusions

We conclude that our suggested formalism can be used to easily specify, simulate and analyze complex biological systems, and supports modular description of local biological dynamics that can be later used as “black boxes” in a larger scope, thus enabling a gradual and hierarchical definition and simulation of complex biological systems. The simple, yet robust formalism enables to target a broad class of stochastic dynamic behaviors, especially those that can be modeled using global environmental feedback regulation rather than direct interaction between individuals.

Comparing algorithms that reconstruct cell lineage trees utilizing information on microsatellite mutations

Organism cells proliferate and die to build, maintain, renew and repair it. The cellular history of an organism up to any point in time can be captured by a cell lineage tree in which vertices represent all organism cells, past and present, and directed edges represent progeny relations among them. The root represents the fertilized egg, and the leaves represent extant and dead cells. Somatic mutations accumulated during cell division endow each organism cell with a genomic signature that is unique with a very high probability. Distances between such genomic signatures can be used to reconstruct an organism's cell lineage tree. Cell populations possess unique features that are absent or rare in organism populations (e.g., the presence of stem cells and a small number of generations since the zygote) and do not undergo sexual reproduction, hence the reconstruction of cell lineage trees calls for careful examination and adaptation of the standard tools of population genetics. Our lab developed a method for reconstructing cell lineage trees by examining only mutations in highly variable microsatellite loci (MS, also called short tandem repeats, STR). In this study we use experimental data on somatic mutations in MS of individual cells in human and mice in order to validate and quantify the utility of known lineage tree reconstruction algorithms in this context. We employed extensive measurements of somatic mutations in individual cells which were isolated from healthy and diseased tissues of mice and humans. The validation was done by analyzing the ability to infer known and clear biological scenarios. In general, we found that if the biological scenario is simple, almost all algorithms tested can infer it. Another somewhat surprising conclusion is that the best algorithm among those tested is Neighbor Joining where the distance measure used is normalized absolute distance. We include our full dataset in Tables S1, S2, S3, S4, S5 to enable further analysis of this data by others.

Single-cell sequencing-based technologies will revolutionize whole-organism science

The unabated progress in next-generation sequencing technologies is fostering a wave of new genomics, epigenomics, transcriptomics and proteomics technologies. These sequencing-based technologies are increasingly being targeted to individual cells, which will allow many new and longstanding questions to be addressed. For example, single-cell genomics will help to uncover cell lineage relationships; single-cell transcriptomics will supplant the coarse notion of marker-based cell types; and single-cell epigenomics and proteomics will allow the functional states of individual cells to be analysed. These technologies will become integrated within a decade or so, enabling high-throughput, multi-dimensional analyses of individual cells that will produce detailed knowledge of the cell lineage trees of higher organisms, including humans. Such studies will have important implications for both basic biological research and medicine.

Role of stem/progenitor cells in reparative disorders

Adult stem cells are activated to proliferate and differentiate during normal tissue homeostasis as well as in disease states and injury. This activation is a vital component in the restoration of function to damaged tissue via either complete or partial regeneration. When regeneration does not fully occur, reparative processes involving an overproduction of stromal components ensure the continuity of tissue at the expense of its normal structure and function, resulting in a “reparative disorder”. Adult stem cells from multiple organs have been identified as being involved in this process and their role in tissue repair is being investigated. Evidence for the participation of mesenchymal stromal cells (MSCs) in the tissue repair process across multiple tissues is overwhelming and their role in reparative disorders is clearly demonstrated, as is the involvement of a number of specific signaling pathways. Transforming growth factor beta, bone morphogenic protein and Wnt pathways interact to form a complex signaling network that is critical in regulating the fate choices of both stromal and tissue-specific resident stem cells (TSCs), determining whether functional regeneration or the formation of scar tissue follows an injury. A growing understanding of both TSCs, MSCs and the complex cascade of signals regulating both cell populations have, therefore, emerged as potential therapeutic targets to treat reparative disorders. This review focuses on recent advances on the role of these cells in skeletal muscle, heart and lung tissues.

Hypoxia increases mouse satellite cell clone proliferation maintaining both in vitro and in vivo heterogeneity and myogenic potential

Satellite cells (SCs) are essential for postnatal muscle growth and regeneration, however, their expansion potential in vitro is limited. Recently, hypoxia has been used to enhance proliferative abilities in vitro of various primary cultures. Here, by isolating SCs from single mouse hindlimb skeletal myofibers, we were able to distinguish two subpopulations of clonally cultured SCs (Low Proliferative Clones - LPC - and High Proliferative Clones - HPC), which, as shown in rat skeletal muscle, were present at a fixed proportion. In addition, culturing LPC and HPC at a low level of oxygen we observed a two fold increased proliferation both for LPC and HPC. LPC showed higher myogenic regulatory factor (MRF) expression than HPC, particularly under the hypoxic condition. Notably, a different myogenic potential between LPC and HPC was retained in vivo: green fluorescent protein (GFP)+LPC transplantation in cardiotoxin-injured Tibialis Anterior led to a higher number of new GFP+muscle fibers per transplanted cell than GFP+HPC. Interestingly, the in vivo myogenic potential of a single cell from an LPC is similar if cultured both in normoxia and hypoxia. Therefore, starting from a single satellite cell, hypoxia allows a larger expansion of LPC than normal O₂ conditions, obtaining a consistent amount of cells for transplantation, but maintaining their myogenic regeneration potential.

Cell lineage analysis of acute leukemia relapse uncovers the role of replication-rate heterogeneity and microsatellite instability

Human cancers display substantial intratumoral genetic heterogeneity, which facilitates tumor survival under changing microenvironmental conditions. Tumor substructure and its effect on disease progression and relapse are incompletely understood. In the present study, a high-throughput method that uses neutral somatic mutations accumulated in individual cells to reconstruct cell lineage trees was applied to hundreds of cells of human acute leukemia harvested from multiple patients at diagnosis and at relapse. The reconstructed cell lineage trees of patients with acute myeloid leukemia showed that leukemia cells at relapse were shallow (divide rarely) compared with cells at diagnosis and were closely related to their stem cell subpopulation, implying that in these instances relapse might have originated from rarely dividing stem cells. In contrast, among patients with acute lymphoid leukemia, no differences in cell depth were observed between diagnosis and relapse. In one case of chronic myeloid leukemia, at blast crisis, most of the cells at relapse were mismatch-repair deficient. In almost all leukemia cases, > 1 lineage was observed at relapse, indicating that diverse mechanisms can promote relapse in the same patient. In conclusion, diverse relapse mechanisms can be observed by systematic reconstruction of cell lineage trees of patients with leukemia.