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The precise lineage relationship between innate lymphoid cells (ILCs) and lymphoid tissue-inducer (LTi) cells is poorly understood. Using single-cell multiplex transcriptional analysis of 100 lymphoid genes and single-cell cultures of fetal liver precursor cells, we identified the common proximal precursor to these lineages and found that its bifurcation was marked by differential induction of the transcription factors PLZF and TCF1. Acquisition of individual effector programs specific to the ILC subsets ILC1, ILC2 and ILC3 was initiated later, at the common ILC precursor stage, by transient expression of mixed ILC1, ILC2 and ILC3 transcriptional patterns, whereas, in contrast, the development of LTi cells did not go through multilineage priming. Our findings provide insight into the divergent mechanisms of the differentiation of the ILC lineage and LTi cell lineage and establish a high-resolution 'blueprint' of their development.
Glioblastoma, the predominant adult malignant brain tumor, has been computationally classified into molecular subtypes whose functional relevance remains to be comprehensively established. Tumors from genetically engineered glioblastoma mouse models initiated by identical driver mutations in distinct cells of origin portray unique transcriptional profiles reflective of their respective lineage. Here, we identify corresponding transcriptional profiles in human glioblastoma and describe patient-derived xenografts with species-conserved subtype-discriminating functional properties. The oligodendrocyte lineage-associated glioblastoma subtype requires functional ERBB3 and harbors unique therapeutic sensitivities. These results highlight the importance of cell lineage in glioblastoma independent of driver mutations and provide a methodology for functional glioblastoma classification for future clinical investigations.
Regulatory T (Treg) cells are a distinct T-cell lineage characterized by sustained Foxp3 expression and potent suppressor function, but the upstream dominant factors that preserve Treg lineage-specific features are mostly unknown. Here, we show that Lkb1 maintains Treg cell lineage identity by stabilizing Foxp3 expression and enforcing suppressor function. Upon T-cell receptor (TCR) stimulation Lkb1 protein expression is upregulated in Treg cells but not in conventional T cells. Mice with Treg cell-specific deletion of Lkb1 develop a fatal early-onset autoimmune disease, with no Foxp3 expression in most Treg cells. Lkb1 stabilizes Foxp3 expression by preventing STAT4-mediated methylation of the conserved noncoding sequence 2 (CNS2) in the Foxp3 locus. Independent of maintaining Foxp3 expression, Lkb1 programs the expression of a wide spectrum of immunosuppressive genes, through mechanisms involving the augmentation of TGF-β signalling. These findings identify a critical function of Lkb1 in maintaining Treg cell lineage identity.
The morphology and physiology of neurons are directed by developmental decisions made within their lines of descent from single stem cells. Distinct stem cells may produce neurons having shared properties that define their cell class, such as the type of secreted neurotransmitter. The relationship between cell class and lineage is complex. Here we developed the transgenic cell class-lineage intersection (CLIn) system to assign cells of a particular class to specific lineages within the Drosophila brain. CLIn also enables birth-order analysis and genetic manipulation of particular cell classes arising from particular lineages. We demonstrated the power of CLIn in the context of the eight central brain type II lineages, which produce highly diverse progeny through intermediate neural progenitors. We mapped 18 dopaminergic neurons from three distinct clusters to six type II lineages that show lineage-characteristic neurite trajectories. In addition, morphologically distinct dopaminergic neurons are produced within a given lineage, and they arise in an invariant sequence. We also identified type II lineages that produce doublesex- and fruitless-expressing neurons and examined whether female-specific apoptosis in these lineages accounts for the lower number of these neurons in the female brain. Blocking apoptosis in these lineages resulted in more cells in both sexes with males still carrying more cells than females. This argues that sex-specific stem cell fate together with differential progeny apoptosis contribute to the final sexual dimorphism.
The binding of the T cell receptor (TCR) to major histocompatibility complex (MHC) molecules in the thymus determines fates of TCRalphabeta lymphocytes that subsequently home to secondary lymphoid tissue. TCR transgenic models have been used to study thymic selection and lineage commitment. Most TCR transgenic mice express the rearranged TCRalphabeta prematurely at the double negative stage and abnormal TCRalphabeta populations of T cells that are not easily detected in non-transgenic mice have been found in secondary lymphoid tissue of TCR transgenic mice.
Dynamic cell identities underlie flexible developmental programs. The stomatal lineage in the Arabidopsis leaf epidermis features asynchronous and indeterminate divisions that can be modulated by environmental cues. The products of the lineage, stomatal guard cells and pavement cells, regulate plant-atmosphere exchanges, and the epidermis as a whole influences overall leaf growth. How flexibility is encoded in development of the stomatal lineage and how cell fates are coordinated in the leaf are open questions. Here, by leveraging single-cell transcriptomics and molecular genetics, we uncovered models of cell differentiation within Arabidopsis leaf tissue. Profiles across leaf tissues identified points of regulatory congruence. In the stomatal lineage, single-cell resolution resolved underlying cell heterogeneity within early stages and provided a fine-grained profile of guard cell differentiation. Through integration of genome-scale datasets and spatiotemporally precise functional manipulations, we also identified an extended role for the transcriptional regulator SPEECHLESS in reinforcing cell fate commitment.
The cell lineage tree of a multicellular organism represents its history of cell divisions from the very first cell, the zygote. A new method for high-resolution reconstruction of parts of such cell lineage trees was recently developed based on phylogenetic analysis of somatic mutations accumulated during normal development of an organism. In this study we apply this method in mice to reconstruct the lineage trees of distinct cell types. We address for the first time basic questions in developmental biology of higher organisms, namely what is the correlation between the lineage relation among cells and their (1) function, (2) physical proximity and (3) anatomical proximity. We analyzed B-cells, kidney-, mesenchymal- and hematopoietic-stem cells, as well as satellite cells, which are adult skeletal muscle stem cells isolated from their niche on the muscle fibers (myofibers) from various skeletal muscles. Our results demonstrate that all analyzed cell types are intermingled in the lineage tree, indicating that none of these cell types are single exclusive clones. We also show a significant correlation between the physical proximity of satellite cells within muscles and their lineage. Furthermore, we show that satellite cells obtained from a single myofiber are significantly clustered in the lineage tree, reflecting their common developmental origin. Lineage analysis based on somatic mutations enables performing high resolution reconstruction of lineage trees in mice and humans, which can provide fundamental insights to many aspects of their development and tissue maintenance.
Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.
Recent innovations in genetics and imaging are providing the means to reconstruct cell lineages, either by tracking cell divisions using live microscopy, or by deducing the history of cells using molecular recorders. A cell lineage on its own, however, is simply a description of cell divisions as branching events. A major goal of current research is to integrate this description of cell relationships with information about the spatial distribution and identities of the cells those divisions produce. Visualizing, interpreting and exploring these complex data in an intuitive manner requires the development of new tools. Here we present CeLaVi, a web-based visualization tool that allows users to navigate and interact with a representation of cell lineages, whilst simultaneously visualizing the spatial distribution, identities and properties of cells. CeLaVi's principal functions include the ability to explore and manipulate the cell lineage tree; to visualise the spatial distribution of cell clones at different depths of the tree; to colour cells in the 3D viewer based on lineage relationships; to visualise various cell qualities on the 3D viewer (e.g. gene expression, cell type) and to annotate selected cells/clones. All these capabilities are demonstrated with four different example data sets. CeLaVi is available at http://www.celavi.pro.
The tail of the Xenopus tadpole will regenerate following amputation, and all three of the main axial structures - the spinal cord, the notochord and the segmented myotomes - are found in the regenerated tail. We have investigated the cellular origin of each of these three tissue types during regeneration. We produced Xenopus laevis embryos transgenic for the CMV (Simian Cytomegalovirus) promoter driving GFP (Green Fluorescent Protein) ubiquitously throughout the embryo. Single tissues were then specifically labelled by making grafts at the neurula stage from transgenic donors to unlabelled hosts. When the hosts have developed to tadpoles, they carry a region of the appropriate tissue labelled with GFP. These tails were amputated through the labelled region and the distribution of labelled cells in the regenerate was followed. We also labelled myofibres using the Cre-lox method. The results show that the spinal cord and the notochord regenerate from the same tissue type in the stump, with no labelling of other tissues. In the case of the muscle, we show that the myofibres of the regenerate arise from satellite cells and not from the pre-existing myofibres. This shows that metaplasia between differentiated cell types does not occur, and that the process of Xenopus tail regeneration is more akin to tissue renewal in mammals than to urodele tail regeneration.
Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for biomedical research by providing a variety of valuable information with the advancement of computational tools. Lineage analysis based on scRNA-seq provides key insights into the fate of individual cells in various systems. However, such analysis is limited by several technical challenges. On top of the considerable computational expertise and resources, these analyses also require specific types of matching data such as exogenous barcode information or bulk assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) data. To overcome these technical challenges, we developed a user-friendly computational algorithm called "LINEAGE" (label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis). Aiming to screen out endogenous markers of lineage located on mitochondrial reads from label-free scRNA-seq data to conduct lineage inference, LINEAGE integrates a marker selection strategy by feature subspace separation and de novo "low cross-entropy subspaces" identification. In this process, the mutation type and subspace-subspace "cross-entropy" of features were both taken into consideration. LINEAGE outperformed three other methods, which were designed for similar tasks as testified with two standard datasets in terms of biological accuracy and computational efficiency. Applied on a label-free scRNA-seq dataset of BRAF-mutated cancer cells, LINEAGE also revealed genes that contribute to BRAF inhibitor resistance. LINEAGE removes most of the technical hurdles of lineage analysis, which will remarkably accelerate the discovery of the important genes or cell-lineage clusters from scRNA-seq data.
In development, lineage segregation is coordinated in time and space. An important example is the mammalian inner cell mass, in which the primitive endoderm (PrE, founder of the yolk sac) physically segregates from the epiblast (EPI, founder of the fetus). While the molecular requirements have been well studied, the physical mechanisms determining spatial segregation between EPI and PrE remain elusive. Here, we investigate the mechanical basis of EPI and PrE sorting. We find that rather than the differences in static cell surface mechanical parameters as in classical sorting models, it is the differences in surface fluctuations that robustly ensure physical lineage sorting. These differential surface fluctuations systematically correlate with differential cellular fluidity, which we propose together constitute a non-equilibrium sorting mechanism for EPI and PrE lineages. By combining experiments and modeling, we identify cell surface dynamics as a key factor orchestrating the correct spatial segregation of the founder embryonic lineages.
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.
The ability to accurately track cells and particles from images is critical to many biomedical problems. To address this, we developed Lineage Mapper, an open-source tracker for time-lapse images of biological cells, colonies, and particles. Lineage Mapper tracks objects independently of the segmentation method, detects mitosis in confluence, separates cell clumps mistakenly segmented as a single cell, provides accuracy and scalability even on terabyte-sized datasets, and creates division and/or fusion lineages. Lineage Mapper has been tested and validated on multiple biological and simulated problems. The software is available in ImageJ and Matlab at isg.nist.gov.
Mosaic mutations can be used to track cell ancestries and reconstruct high-resolution lineage trees during cancer progression and during development, starting from the first cell divisions of the zygote. However, this approach requires sampling and analyzing the genomes of multiple cells, which can be redundant in lineage representation, limiting the scalability of the approach. We describe a strategy for cost- and time-efficient lineage reconstruction using clonal induced pluripotent stem cell lines from human skin fibroblasts. The approach leverages shallow sequencing coverage to assess the clonality of the lines, clusters redundant lines and sums their coverage to accurately discover mutations in the corresponding lineages. Only a fraction of lines needs to be sequenced to high coverage. We demonstrate the effectiveness of this approach for reconstructing lineage trees during development and in hematologic malignancies. We discuss and propose an optimal experimental design for reconstructing lineage trees.
T cells, as well as other cell types, are composed of phenotypically and functionally distinct subsets. However, for many of these populations it is unclear whether they develop from common or separate progenitors. To address such issues, we developed a novel approach, termed cellular barcoding, that allows the dissection of lineage relationships. We demonstrate that the labeling of cells with unique identifiers coupled to a microarray-based detection system can be used to analyze family relationships between the progeny of such cells. To exemplify the potential of this technique, we studied migration patterns of families of antigen-specific CD8(+) T cells in vivo. We demonstrate that progeny of individual T cells rapidly seed independent lymph nodes and that antigen-specific CD8(+) T cells present at different effector sites are largely derived from a common pool of precursors. These data show how locally primed T cells disperse and provide a technology for kinship analysis with wider utility.
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.
Early events during development leading to exit from a pluripotent state and commitment toward a specific germ layer still need in-depth understanding. Autophagy has been shown to play a crucial role in both development and differentiation. This study employs human embryonic and induced pluripotent stem cells to understand the early events of lineage commitment with respect to the role of autophagy in this process. Our data indicate that a dip in autophagy facilitates exit from pluripotency. Upon exit, we demonstrate that the modulation of autophagy affects SOX2 levels and lineage commitment, with induction of autophagy promoting SOX2 degradation and mesendoderm formation, whereas inhibition of autophagy causes SOX2 accumulation and neuroectoderm formation. Thus, our results indicate that autophagy-mediated SOX2 turnover is a determining factor for lineage commitment. These findings will deepen our understanding of development and lead to improved methods to derive different lineages and cell types.Abbreviations: ACTB: Actin, beta; ATG: Autophagy-related; BafA1: Bafilomycin A1; CAS9: CRISPR-associated protein 9; CQ: Chloroquine; DE: Definitive endoderm; hESCs: Human Embryonic Stem Cells; hiPSCs: Human Induced Pluripotent Stem Cells; LAMP1: Lysosomal Associated Membrane Protein 1; MAP1LC3: Microtubule-Associated Protein 1 Light Chain 3; MTOR: Mechanistic Target Of Rapamycin Kinase; NANOG: Nanog Homeobox; PAX6: Paired Box 6; PE: Phosphatidylethanolamine; POU5F1: POU class 5 Homeobox 1; PRKAA2: Protein Kinase AMP-Activated Catalytic Subunit Alpha 2; SOX2: SRY-box Transcription Factor 2; SQSTM1: Sequestosome 1; ULK1: unc-51 like Autophagy Activating Kinase 1; WDFY3: WD Repeat and FYVE Domain Containing 3.
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.
Developmental systems biology is poised to exploit large-scale data from two approaches: genomics and live imaging. The combination of the two offers the opportunity to map gene functions and gene networks in vivo at single-cell resolution using cell tracking and quantification of cellular phenotypes. Here we present Digital Development (http://www.digital-development.org), a database of cell lineage differentiation with curated phenotypes, cell-specific gene functions and a multiscale model. The database stores data from recent systematic studies of cell lineage differentiation in the C. elegans embryo containing ∼ 200 conserved genes, 1400 perturbed cell lineages and 600,000 digitized single cells. Users can conveniently browse, search and download four categories of phenotypic and functional information from an intuitive web interface. This information includes lineage differentiation phenotypes, cell-specific gene functions, differentiation landscapes and fate choices, and a multiscale model of lineage differentiation. Digital Development provides a comprehensive, curated, multidimensional database for developmental biology. The scale, resolution and richness of biological information presented here facilitate exploration of gene-specific and systems-level mechanisms of lineage differentiation in Metazoans.
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