Reconstruction of cell lineage trees in mice

Mesenchymal stem cell differentiation and usage for biotechnology applications: tissue engineering and food manufacturing

Recent advances in the field of stem cell research now enable their utilisation for biotechnology applications in regenerative medicine and food tech. The first use of stem cells as biomedical devices employed a combination of cells and scaffold to restore, improve, or replace damaged tissues and to grow new viable tissue for replacement organs. This approach has also been adopted to replace meat production in the food industry. Mesenchymal stem cells are the source material used to induce cells to differentiate into the desired lineage. These technologies require mass propagation and rely on supplying the regulatory factors that direct differentiation. Mesenchymal stem cells can differentiate into fibroblastic and skeletal cells; fibroblastic/chondrogenic/osteogenic/myogenic and adipogenic lineages. Each differentiation fate requires specific key molecular regulators and appropriate activation conditions. Stem cell commitment determination involves a concerted effort of coordinated activation and silencing of lineage-specific genes. Transcription factors which bind gene promoters and chromatin-remodelling proteins are key players in the control process of lineage commitment and differentiation from embryogenesis through adulthood. Consequently, a major research challenge is to characterise such molecular pathways that coordinate lineage-specific differentiation and function. Revealing the mechanisms of action and the main factors will provide the knowledge necessary to control activation and regulation to achieve a specific lineage. Growing cells on a scaffold is a support system that mimics natural tissue and transduces the appropriate signals of the tissue niche for appropriate cellular function. The outcome of such research will deepen the understanding of cell differentiation to promote and advance the biotech, allowing the cell expansion required for their usage in therapy or the development of food tech.

Applications of Single-Cell DNA Sequencing

Over the past decade, genomic analyses of single cells-the fundamental units of life-have become possible. Single-cell DNA sequencing has shed light on biological questions that were previously inaccessible across diverse fields of research, including somatic mutagenesis, organismal development, genome function, and microbiology. Single-cell DNA sequencing also promises significant future biomedical and clinical impact, spanning oncology, fertility, and beyond. While single-cell approaches that profile RNA and protein have greatly expanded our understanding of cellular diversity, many fundamental questions in biology and important biomedical applications require analysis of the DNA of single cells. Here, we review the applications and biological questions for which single-cell DNA sequencing is uniquely suited or required. We include a discussion of the fields that will be impacted by single-cell DNA sequencing as the technology continues to advance.

Deciphering neural heterogeneity through cell lineage tracing

Understanding how an adult brain reaches an appropriate size and cell composition from a pool of progenitors that proliferates and differentiates is a key question in Developmental Neurobiology. Not only the control of final size but also, the proper arrangement of cells of different embryonic origins is fundamental in this process. Each neural progenitor has to produce a precise number of sibling cells that establish clones, and all these clones will come together to form the functional adult nervous system. Lineage cell tracing is a complex and challenging process that aims to reconstruct the offspring that arise from a single progenitor cell. This tracing can be achieved through strategies based on genetically modified organisms, using either genetic tracers, transfected viral vectors or DNA constructs, and even single-cell sequencing. Combining different reporter proteins and the use of transgenic mice revolutionized clonal analysis more than a decade ago and now, the availability of novel genome editing tools and single-cell sequencing techniques has vastly improved the capacity of lineage tracing to decipher progenitor potential. This review brings together the strategies used to study cell lineages in the brain and the role they have played in our understanding of the functional clonal relationships among neural cells. In addition, future perspectives regarding the study of cell heterogeneity and the ontogeny of different cell lineages will also be addressed.

Clonal tracking using embedded viral barcoding and high-throughput sequencing

Embedded viral barcoding in combination with high-throughput sequencing is a powerful technology with which to track single-cell clones. It can provide clonal-level insights into cellular proliferation, development, differentiation, migration, and treatment efficacy. Here, we present a detailed protocol for a viral barcoding procedure that includes the creation of barcode libraries, the viral delivery of barcodes, the recovery of barcodes, and the computational analysis of barcode sequencing data. The entire procedure can be completed within a few weeks. This barcoding method requires cells to be susceptible to viral transduction. It provides high sensitivity and throughput, and enables precise quantification of cellular progeny. It is cost efficient and does not require any advanced skills. It can also be easily adapted to many types of applications, including both in vitro and in vivo experiments.

High-throughput dense reconstruction of cell lineages

The first meeting exclusively dedicated to the 'High-throughput dense reconstruction of cell lineages' took place at Janelia Research Campus (Howard Hughes Medical Institute) from 14 to 18 April 2019. Organized by Tzumin Lee, Connie Cepko, Jorge Garcia-Marques and Isabel Espinosa-Medina, this meeting echoed the recent eruption of new tools that allow the reconstruction of lineages based on the phylogenetic analysis of DNA mutations induced during development. Combined with single-cell RNA sequencing, these tools promise to solve the lineage of complex model organisms at single-cell resolution. Here, we compile the conference consensus on the technological and computational challenges emerging from the use of the new strategies, as well as potential solutions.

Novel Technological Advances in Functional Connectomics in C. elegans

The complete structure and connectivity of the Caenorhabditis elegans nervous system ("mind of a worm") was first published in 1986, representing a critical milestone in the field of connectomics. The reconstruction of the nervous system (connectome) at the level of synapses provided a unique perspective of understanding how behavior can be coded within the nervous system. The following decades have seen the development of technologies that help understand how neural activity patterns are connected to behavior and modulated by sensory input. Investigations on the developmental origins of the connectome highlight the importance of role of neuronal cell lineages in the final connectivity matrix of the nervous system. Computational modeling of neuronal dynamics not only helps reconstruct the biophysical properties of individual neurons but also allows for subsequent reconstruction of whole-organism neuronal network models. Hence, combining experimental datasets with theoretical modeling of neurons generates a better understanding of organismal behavior. This review discusses some recent technological advances used to analyze and perturb whole-organism neuronal function along with developments in computational modeling, which allows for interrogation of both local and global neural circuits, leading to different behaviors. Combining these approaches will shed light into how neural networks process sensory information to generate the appropriate behavioral output, providing a complete understanding of the worm nervous system.

Maps of variability in cell lineage trees

New approaches to lineage tracking have allowed the study of differentiation in multicellular organisms over many generations of cells. Understanding the phenotypic variability observed in these lineage trees requires new statistical methods. Whereas an invariant cell lineage, such as that for the nematode Caenorhabditis elegans, can be described by a lineage map, defined as the pattern of phenotypes overlaid onto the binary tree, a traditional lineage map is static and does not describe the variability inherent in the cell lineages of higher organisms. Here, we introduce lineage variability maps which describe the pattern of second-order variation in lineage trees. These maps can be undirected graphs of the partial correlations between every lineal position, or directed graphs showing the dynamics of bifurcated patterns in each subtree. We show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. This required developing the generalized spectral analysis for a binary tree, the natural framework for describing tree-structured variation. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the variability maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show that most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.

Quantitative Analysis of Synthetic Cell Lineage Tracing Using Nuclease Barcoding

Lineage tracing by the determination and mapping of progeny arising from single cells is an important approach enabling the elucidation of mechanisms underlying diverse biological processes ranging from development to disease. We developed a dynamic sequence-based barcode system for synthetic lineage tracing and have demonstrated its performance in C. elegans, a model organism whose lineage tree is well established. The strategy we use creates lineage trees based upon the introduction of synthetically controlled mutations into cells and the propagation of these mutations to daughter cells at each cell division. We analyzed this experimental proof of concept along with a corresponding simulation and analytical model to gain a deeper understanding of the coding capacity of the system. Our results provide specific bounds on the fidelity of lineage tracing using such approaches.

Synthetic recording and in situ readout of lineage information in single cells

Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. Many biological processes of interest occur in optically opaque or physically inaccessible contexts, necessitating approaches other than direct imaging. Here we describe a synthetic system that enables cells to record lineage information and event histories in the genome in a format that can be subsequently read out of single cells in situ. This system, termed memory by engineered mutagenesis with optical in situ readout (MEMOIR), is based on a set of barcoded recording elements termed scratchpads. The state of a given scratchpad can be irreversibly altered by CRISPR/Cas9-based targeted mutagenesis, and later read out in single cells through multiplexed single-molecule RNA fluorescence hybridization (smFISH). Using MEMOIR as a proof of principle, we engineered mouse embryonic stem cells to contain multiple scratchpads and other recording components. In these cells, scratchpads were altered in a progressive and stochastic fashion as the cells proliferated. Analysis of the final states of scratchpads in single cells in situ enabled reconstruction of lineage information from cell colonies. Combining analysis of endogenous gene expression with lineage reconstruction in the same cells further allowed inference of the dynamic rates at which embryonic stem cells switch between two gene expression states. Finally, using simulations, we show how parallel MEMOIR systems operating in the same cell could enable recording and readout of dynamic cellular event histories. MEMOIR thus provides a versatile platform for information recording and in situ, single-cell readout across diverse biological systems.

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 analysis by microsatellite loci deep sequencing in mice

Lineage analysis is the identification of all the progeny of a single progenitor cell, and has become particularly useful for studying developmental processes and cancer biology. Here, we propose a novel and effective method for lineage analysis that combines sequence capture and next-generation sequencing technology. Genome-wide mononucleotide and dinucleotide microsatellite loci in eight samples from two mice were identified and used to construct phylogenetic trees based on somatic indel mutations at these loci, which were unique enough to distinguish and parse samples from different mice into different groups along the lineage tree. For example, biopsies from the liver and stomach, which originate from the endoderm, were located in the same clade, while samples in kidney, which originate from the mesoderm, were located in another clade. Yet, tissue with a common developmental origin may still contain cells of a mixed ancestry. This genome-wide approach thus provides a non-invasive lineage analysis method based on mutations that accumulate in the genomes of opaque multicellular organism somatic cells. Mol. Reprod. Dev. 83: 387-391, 2016. © 2016 Wiley Periodicals, Inc.

Concise review: understanding clonal dynamics in homeostasis and injury through multicolor lineage tracing

Lineage tracing is an essential tool to study stem cell fate. Although traditional lineage tracing techniques have considerably advanced our understanding of stem cell behavior, they pose significant limitations for identification and longitudinal tracking of the progeny of individual stem cells, to compare their behaviors. This is of importance given the well-established heterogeneity among stem cells both in terms of potentialities and proliferative capacities. The recent development of multicolor genetic reporters addressable to specific cell populations largely overcomes these issues. These new "rainbow" technologies provide increased resolution in clonal identification and offer the possibility to study the relative distribution, contacts, tiled arrangement, and competitive interactions among cells or groups of cells of the same type.

Lessons from mouse chimaera experiments with a reiterated transgene marker: revised marker criteria and a review of chimaera markers

Recent reports of a new generation of ubiquitous transgenic chimaera markers prompted us to consider the criteria used to evaluate new chimaera markers and develop more objective assessment methods. To investigate this experimentally we used several series of fetal and adult chimaeras, carrying an older, multi-copy transgenic marker. We used two additional independent markers and objective, quantitative criteria for cell selection and cell mixing to investigate quantitative and spatial aspects of developmental neutrality. We also suggest how the quantitative analysis we used could be simplified for future use with other markers. As a result, we recommend a five-step procedure for investigators to evaluate new chimaera markers based partly on criteria proposed previously but with a greater emphasis on examining the developmental neutrality of prospective new markers. These five steps comprise (1) review of published information, (2) evaluation of marker detection, (3) genetic crosses to check for effects on viability and growth, (4) comparisons of chimaeras with and without the marker and (5) analysis of chimaeras with both cell populations labelled. Finally, we review a number of different chimaera markers and evaluate them using the extended set of criteria. These comparisons indicate that, although the new generation of ubiquitous fluorescent markers are the best of those currently available and fulfil most of the criteria required of a chimaera marker, further work is required to determine whether they are developmentally neutral.

Genome sequencing of normal cells reveals developmental lineages and mutational processes

The somatic mutations present in the genome of a cell accumulate over the lifetime of a multicellular organism. These mutations can provide insights into the developmental lineage tree, the number of divisions that each cell has undergone and the mutational processes that have been operative. Here we describe whole genomes of clonal lines derived from multiple tissues of healthy mice. Using somatic base substitutions, we reconstructed the early cell divisions of each animal, demonstrating the contributions of embryonic cells to adult tissues. Differences were observed between tissues in the numbers and types of mutations accumulated by each cell, which likely reflect differences in the number of cell divisions they have undergone and varying contributions of different mutational processes. If somatic mutation rates are similar to those in mice, the results indicate that precise insights into development and mutagenesis of normal human cells will be possible.

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.

Determinative developmental cell lineages are robust to cell deaths

All forms of life are confronted with environmental and genetic perturbations, making phenotypic robustness an important characteristic of life. Although development has long been viewed as a key component of phenotypic robustness, the underlying mechanism is unclear. Here we report that the determinative developmental cell lineages of two protostomes and one deuterostome are structured such that the resulting cellular compositions of the organisms are only modestly affected by cell deaths. Several features of the cell lineages, including their shallowness, topology, early ontogenic appearances of rare cells, and non-clonality of most cell types, underlie the robustness. Simple simulations of cell lineage evolution demonstrate the possibility that the observed robustness arose as an adaptation in the face of random cell deaths in development. These results reveal general organizing principles of determinative developmental cell lineages and a conceptually new mechanism of phenotypic robustness, both of which have important implications for development and evolution.

Hypermutable DNA chronicles the evolution of human colon cancer

Intratumor genetic heterogeneity reflects the evolutionary history of a cancer and is thought to influence treatment outcomes. Here we report that a simple PCR-based assay interrogating somatic variation in hypermutable polyguanine (poly-G) repeats can provide a rapid and reliable assessment of mitotic history and clonal architecture in human cancer. We use poly-G repeat genotyping to study the evolution of colon carcinoma. In a cohort of 22 patients, we detect poly-G variants in 91% of tumors. Patient age is positively correlated with somatic mutation frequency, suggesting that some poly-G variants accumulate before the onset of carcinogenesis during normal division in colonic stem cells. Poorly differentiated tumors have fewer mutations than well-differentiated tumors, possibly indicating a shorter mitotic history of the founder cell in these cancers. We generate poly-G mutation profiles of spatially separated samples from primary carcinomas and matched metastases to build well-supported phylogenetic trees that illuminate individual patients' path of metastatic progression. Our results show varying degrees of intratumor heterogeneity among patients. Finally, we show that poly-G mutations can be found in other cancers than colon carcinoma. Our approach can generate reliable maps of intratumor heterogeneity in large numbers of patients with minimal time and cost expenditure.

Multiplex cell and lineage tracking with combinatorial labels

We present a method to label and trace the lineage of multiple neural progenitors simultaneously in vertebrate animals via multiaddressable genome-integrative color (MAGIC) markers. We achieve permanent expression of combinatorial labels from new Brainbow transgenes introduced in embryonic neural progenitors with electroporation of transposon vectors. In the mouse forebrain and chicken spinal cord, this approach allows us to track neural progenitor's descent during pre- and postnatal neurogenesis or perinatal gliogenesis in long-term experiments. Color labels delineate cytoarchitecture, resolve spatially intermixed clones, and specify the lineage of astroglial subtypes and adult neural stem cells. Combining colors and subcellular locations provides an expanded marker palette to individualize clones. We show that this approach is also applicable to modulate specific signaling pathways in a mosaic manner while color-coding the status of individual cells regarding induced molecular perturbations. This method opens new avenues for clonal and functional analysis in varied experimental models and contexts.

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.

Moderate-intensity treadmill running promotes expansion of the satellite cell pool in young and old mice

Satellite cells, the myogenic progenitors located at the myofibre surface, are essential for the repair of adult skeletal muscle. There is ample evidence for an age-linked decline in the number of satellite cells and performance in limb muscles. Hence, an effective means of activating and expanding the satellite cell pool may enhance muscle maintenance and reduce the impact of age-associated muscle deterioration (sarcopaenia). Accordingly, in the present study, we explored the beneficial effects of endurance exercise on satellite cells in young and old mice. Animals were subjected to an 8-week moderate-intensity treadmill-running approach that does not inflict apparent muscle damage (0° inclination, 11.5 m·min(-1) for 30 min·day(-1) , 6 days·week(-1) ). Myofibres of extensor digitorum longus muscles were then isolated from exercised and sedentary mice and used for monitoring the number of satellite cells, as well as for harvesting individual satellite cells for clonal growth assays. We specifically focused on satellite cell pools of single myofibres, with the view that daily wear of muscles probably affects individual myofibres rather than causing overall muscle damage. We found an expansion of the satellite cell pool in the exercised groups compared to the sedentary groups, with the same increase (~ 1.6-fold) in both ages. The results of the present study are in agreement with our findings obtained using rat gastrocnemius, indicating the consistent effect of exercise on satellite cell expansion in limb muscles. The experimental paradigm established in the present study is useful for investigating satellite cell dynamics at the myofibre niche, as well as for broader investigations of the impact of physiologically and pathologically relevant factors on adult myogenesis.

Use of somatic mutations to quantify random contributions to mouse development

Background

The C. elegans cell fate map, in which the lineage of its approximately 1000 cells is visibly charted beginning from the zygote, represents a developmental biology milestone. Nematode development is invariant from one specimen to the next, whereas in mammals, aspects of development are probabilistic, and development exhibits variation between even genetically identical individuals. Consequently, a single defined cell fate map applicable to all individuals cannot exist.

Results

To determine the extent to which patterns of cell lineage are conserved between different mice, we have employed the recently developed method of “phylogenetic fate mapping” to compare cell fate maps in siblings. In this approach, somatic mutations arising in individual cells are used to retrospectively deduce lineage relationships through phylogenetic and—as newly investigated here—related analytical approaches based on genetic distance. We have cataloged genomic mutations at an average of 110 mutation-prone polyguanine (polyG) tracts for about 100 cells clonally isolated from various corresponding tissues of each of two littermates of a hypermutable mouse strain.

Conclusions

We find that during mouse development, muscle and fat arise from a mixed progenitor cell pool in the germ layer, but, contrastingly, vascular endothelium in brain derives from a smaller source of progenitor cells. Additionally, formation of tissue primordia is marked by establishment of left and right lateral compartments, with restricted cell migration between divisions. We quantitatively demonstrate that development represents a combination of stochastic and deterministic events, offering insight into how chance influences normal development and may give rise to birth defects.

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.

Cell lineage analysis of the mammalian female germline

Fundamental aspects of embryonic and post-natal development, including maintenance of the mammalian female germline, are largely unknown. Here we employ a retrospective, phylogenetic-based method for reconstructing cell lineage trees utilizing somatic mutations accumulated in microsatellites, to study female germline dynamics in mice. Reconstructed cell lineage trees can be used to estimate lineage relationships between different cell types, as well as cell depth (number of cell divisions since the zygote). We show that, in the reconstructed mouse cell lineage trees, oocytes form clusters that are separate from hematopoietic and mesenchymal stem cells, both in young and old mice, indicating that these populations belong to distinct lineages. Furthermore, while cumulus cells sampled from different ovarian follicles are distinctly clustered on the reconstructed trees, oocytes from the left and right ovaries are not, suggesting a mixing of their progenitor pools. We also observed an increase in oocyte depth with mouse age, which can be explained either by depth-guided selection of oocytes for ovulation or by post-natal renewal. Overall, our study sheds light on substantial novel aspects of female germline preservation and development.

Many aspects of mammalian female germline development during embryogenesis and throughout adulthood are either unknown or under debate. In this study we applied a novel method for the reconstruction of cell lineage trees utilizing microsatellite mutations, accumulated during mouse life, in oocytes and other cells, sampled from young and old mice. Analysis of the reconstructed cell lineage trees shows that oocytes are clustered separately from bone-marrow derived cells, that oocytes from different ovaries share common progenitors, and that oocyte depth (number of cell divisions since the zygote) increases significantly with mouse age.

New insights into cortical interneurons development and classification: contribution of developmental studies

The concerted development of GABAergic interneurons and glutamatergic neurons is a key feature in the construction of the cerebral cortex. In contrast with glutamatergic neurons, GABAergic interneurons are heterogeneous differing by their axonal and dendritic morphologies, biochemical markers, connectivity, and physiology. Furthermore, interneurons have recently been shown to be generated in a variety of telencephalic structures (the ganglionic eminences, the entopeduncular and preoptic areas and the cortex). This review describes the origin, specification and differentiation of interneurons. These recent developmental studies may help to clarify the classification of mature interneurons. In particular recent studies, including our own, provide compelling evidences that most interneurons are specify after their last division in their region of origin before migration. The roles of target tissues in determining the final physiological properties of interneurons are also discussed.

Decoding cell lineage from acquired mutations using arbitrary deep sequencing

Because mutations are inevitable, the genome of each cell in a multicellular organism becomes unique and therefore encodes a record of its ancestry. Here we coupled arbitrary single primer PCR with next-generation DNA sequencing to catalog mutations and deconvolve the phylogeny of cultured mouse cells. This study helps pave the way toward construction of retrospective cell-fate maps based on mutations accumulating in genomes of somatic cells.

Tracing cells for tracking cell lineage and clonal behavior

Reconstructing the lineage of cells is central to understanding development and is now also an important issue in stem cell research. Technological advances in genetically engineered permanent cell labeling, together with a multiplicity of fluorescent markers and sophisticated imaging, open new possibilities for prospective and retrospective clonal analysis.

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

Myofiber cultures give rise to myogenic as well as to non-myogenic cells. Whether these myofiber-associated non-myogenic cells develop from resident stem cells that possess mesenchymal plasticity or from other stem cells such as mesenchymal stem cells (MSCs) remain unsolved. To address this question, we applied a method for reconstructing cell lineage trees from somatic mutations to MSCs and myogenic and non-myogenic cells from individual myofibers that were cultured at clonal density.

Our analyses show that (i) in addition to myogenic progenitors, myofibers also harbor non-myogenic progenitors of a distinct, yet close, lineage; (ii) myofiber-associated non-myogenic and myogenic cells share the same muscle-bound primordial stem cells of a lineage distinct from bone marrow MSCs; (iii) these muscle-bound primordial stem-cells first part to individual muscles and then differentiate into myogenic and non-myogenic stem cells.

Colon stem cell and crypt dynamics exposed by cell lineage reconstruction

Stem cell dynamics in vivo are often being studied by lineage tracing methods. Our laboratory has previously developed a retrospective method for reconstructing cell lineage trees from somatic mutations accumulated in microsatellites. This method was applied here to explore different aspects of stem cell dynamics in the mouse colon without the use of stem cell markers. We first demonstrated the reliability of our method for the study of stem cells by confirming previously established facts, and then we addressed open questions. Our findings confirmed that colon crypts are monoclonal and that, throughout adulthood, the process of monoclonal conversion plays a major role in the maintenance of crypts. The absence of immortal strand mechanism in crypts stem cells was validated by the age-dependent accumulation of microsatellite mutations. In addition, we confirmed the positive correlation between physical and lineage proximity of crypts, by showing that the colon is separated into small domains that share a common ancestor. We gained new data demonstrating that colon epithelium is clustered separately from hematopoietic and other cell types, indicating that the colon is constituted of few progenitors and ruling out significant renewal of colonic epithelium from hematopoietic cells during adulthood. Overall, our study demonstrates the reliability of cell lineage reconstruction for the study of stem cell dynamics, and it further addresses open questions in colon stem cells. In addition, this method can be applied to study stem cell dynamics in other systems.

The study of stem cell and tissue dynamics in vivo is often carried out by lineage tracing methods that depend on the presence of specific markers and on the availability of stem cells. In the current study, we applied a novel method for the reconstruction of cell lineage trees from microsatellite mutations accumulated during mouse life. We focused on the intestinal epithelium, since its stem cells were intensively studied by various tracing methods that clarified many aspects of their dynamics. We first showed the reliability of our method by confirming three previously established facts: the existence of “monoclonal conversion,” the absence of an immortal strand mechanism in colon stem cells, and the separation of the colon into small domains each with a common ancestor. We also answered a few open questions, showing that the colon's lineage is separated from other lineages such as the hematopoietic and pancreatic lineages. Overall, our work presents a new approach for the study of stem cell dynamics and can similarly be used for studying stem cell dynamics in other systems.

Maximum parsimony analysis of gene expression profiles permits the reconstruction of developmental cell lineage trees

Spatiotemporal control of gene expression lies at the heart of generating several hundred distinct cell types required for the development of higher order animals. Different cell types within complex organs are often characterised by means of genome-wide gene expression profiling, but analogous information for early developmental as well as adult stem and progenitor cells is largely missing because their identity is commonly unknown or they are present in prohibitively small numbers. Here we show that maximum parsimony approaches previously used to reconstruct evolutionary trees from gene content of extant species can be adapted to reconstruct cellular hierarchies both during development and steady state homeostasis of complex mammalian tissues. Using haematopoiesis as a model, we show that developmental trees reconstructed from expression profiles of mature cells are not only consistent with current experimentally validated trees but also have predictive value in determining progenitor cell specific transcriptional programmes and lineage determining transcription factors. Subsequent analysis across diverse developmental systems such as neuronal development and endoderm organogenesis demonstrated that maximum parsimony-based reconstruction of developmental trees represents a widely applicable approach to infer developmental pathways as well as the transcriptional control mechanisms underlying cell fate specification.

Reduced satellite cell numbers and myogenic capacity in aging can be alleviated by endurance exercise

Background

Muscle regeneration depends on satellite cells, myogenic stem cells that reside on the myofiber surface. Reduced numbers and/or decreased myogenic aptitude of these cells may impede proper maintenance and contribute to the age-associated decline in muscle mass and repair capacity. Endurance exercise was shown to improve muscle performance; however, the direct impact on satellite cells in aging was not yet thoroughly determined. Here, we focused on characterizing the effect of moderate-intensity endurance exercise on satellite cell, as possible means to attenuate adverse effects of aging. Young and old rats of both genders underwent 13 weeks of treadmill-running or remained sedentary.

Methodology

Gastrocnemius muscles were assessed for the effect of age, gender and exercise on satellite-cell numbers and myogenic capacity. Satellite cells were identified in freshly isolated myofibers based on Pax7 immunostaining (i.e., ex-vivo). The capacity of individual myofiber-associated cells to produce myogenic progeny was determined in clonal assays (in-vitro). We show an age-associated decrease in satellite-cell numbers and in the percent of myogenic clones in old sedentary rats. Upon exercise, there was an increase in myofibers that contain higher numbers of satellite cells in both young and old rats, and an increase in the percent of myogenic clones derived from old rats. Changes at the satellite cell level in old rats were accompanied with positive effects on the lean-to-fat Gast muscle composition and on spontaneous locomotion levels. The significance of these data is that they suggest that the endurance exercise-mediated boost in both satellite numbers and myogenic properties may improve myofiber maintenance in aging.

Phylogenetic analysis of developmental and postnatal mouse cell lineages

Fate maps depict how cells relate together through past lineage relationships, and are useful tools for studying developmental and somatic processes. However, with existing technologies, it has not been possible to generate detailed fate maps of complex organisms such as the mouse. We and others have therefore proposed a novel approach, "phylogenetic fate mapping," where patterns of somatic mutation carried by the individual cells of an animal are used to retrospectively deduce lineage relationships through phylogenetic inference. Here, we have cataloged genomic polymorphisms at 324 mutation-prone polyguanine tracts for nearly 300 cells isolated from a single mouse, and have explored the cells' lineage relationships both phylogenetically and through a network-based approach. We present a model of mouse embryogenesis, where an early period of substantial cell mixing is followed by more coherent growth of clones later. We find that cells from certain tissues have greater numbers of close relatives in other specific tissues than expected from chance, suggesting that those populations arise from a similar pool of ancestral lineages. Finally, we have investigated the dynamics of cell turnover (the frequency of cell loss and replacement) in postnatal tissues. This work offers a longitudinal study of developmental lineages, from conception to adulthood, and provides insight into basic questions of mouse embryology as well as the somatic processes that occur after birth.

Cell lineage analysis of a mouse tumor

Revealing the lineage relations among cancer cells can shed light on tumor growth patterns and metastasis formation, yet cell lineages have been difficult to come by in the absence of a suitable method. We previously developed a method for reconstructing cell lineage trees from genomic variability caused by somatic mutations. Here, we apply the method to cancer and reconstruct, for the first time, a lineage tree of neoplastic and adjacent normal cells obtained by laser microdissection from tissue sections of a mouse lymphoma. Analysis of the reconstructed tree reveals that the tumor initiated from a single founder cell, approximately 5 months before diagnosis, that the tumor grew in a physically coherent manner, and that the average number of cell divisions accumulated in cancerous cells was almost twice than in adjacent normal lung epithelial cells but slightly less than the expected figure for normal B lymphocytes. The cells were also genotyped at the TP53 locus, and neoplastic cells were found to share a common mutation, which was most likely present in a heterozygous state. Our work shows that the ability to obtain data regarding the physical appearance, precise anatomic position, genotypic profile, and lineage position of single cells may be useful for investigating cancer development, progression, and interaction with the microenvironment.

Estimating cell depth from somatic mutations

The depth of a cell of a multicellular organism is the number of cell divisions it underwent since the zygote, and knowing this basic cell property would help address fundamental problems in several areas of biology. At present, the depths of the vast majority of human and mouse cell types are unknown. Here, we show a method for estimating the depth of a cell by analyzing somatic mutations in its microsatellites, and provide to our knowledge for the first time reliable depth estimates for several cells types in mice. According to our estimates, the average depth of oocytes is 29, consistent with previous estimates. The average depth of B cells ranges from 34 to 79, linearly related to the mouse age, suggesting a rate of one cell division per day. In contrast, various types of adult stem cells underwent on average fewer cell divisions, supporting the notion that adult stem cells are relatively quiescent. Our method for depth estimation opens a window for revealing tissue turnover rates in animals, including humans, which has important implications for our knowledge of the body under physiological and pathological conditions.

All the cells in our body are descendants of a single cell – the fertilized egg. Some cells are relatively close descendants, having undergone a small number of cell divisions, while other cells may be hundreds or even thousands of divisions deep. So far, science was unable to provide even gross estimates for the depths of the vast majority of human and mouse cells. In this study, we show that precise depth estimates of cells can be obtained from the analysis of non-hazardous mutations that spontaneously accumulate during normal development. The concept behind the method is simple: deeper cells tend to acquire more mutations and “drift away” from the original DNA sequence of the fertilized egg. Knowing how deep cells are is the key to many fundamental open questions in biology and medicine, such as whether neurons in our brain can regenerate, or whether new eggs are created in adult females.