scholarly journals Maps of variability in cell lineage trees

2018 ◽  
Author(s):  
Damien G. Hicks ◽  
Terence P. Speed ◽  
Mohammed Yassin ◽  
Sarah M. Russell

AbstractNew approaches to lineage tracking allow the study of cell differentiation over many generations of cells during development in multicellular organisms. Understanding the variability observed in these lineage trees requires new statistical methods. Whereas invariant cell lineages, such as that for the nematode Caenorhabditis elegans, can be described using a lineage map, defined as the fixed pattern of phenotypes overlaid onto the binary tree structure, the variability of cell lineages from higher organisms makes it impossible to draw a single lineage map. Here, we introduce lineage variability maps which describe the pattern of second-order variation throughout the lineage tree. 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. By using the symmetry invariance of a binary tree to develop a generalized spectral analysis for cell lineages, we show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show how 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.Author summaryMulticellular organisms develop from a single fertilized egg by sequential cell divisions. The progeny from these divisions adopt different traits that are transmitted and modified through many generations. By tracking how cell traits change with each successive cell division throughout the family, or lineage, tree, it has been possible to understand where and how these modifications are controlled at the single-cell level, thereby addressing questions about, for example, the developmental origin of tissues, the sources of differentiation in immune cells, or the relationship between primary tumors and metastases. Such lineages often show large variability, with apparently identical founder cells giving rise to different patterns of descendants. Fundamental scientific questions, such as about the range of possible cell types a cell can give rise to, are often about this variability. To characterize this variation, and thus understand the lineage at the population level, we introduce lineage variability maps. Using data from worm and mammalian cell lineages we show how these maps provide quantifiable answers to questions about any developing lineage, such as the potency of founder cells and the progressive restriction of cell fate at each stage in the tree.

2019 ◽  
Author(s):  
Meng Yuan ◽  
Xujiang Yang ◽  
Jinghua Lin ◽  
Xiaolong Cao ◽  
Feng Chen ◽  
...  

ABSTRACTThe developmental cell lineage tree, which records every cell division event and the terminal developmental state of each single cell, is one of the most important traits of multicellular organisms, as well as key to many significant unresolved questions in biology. Recent technological breakthroughs are paving the way for direct determination of cell lineage trees, yet a general framework for the computational analysis of lineage trees, in particular an algorithm to compare two lineage trees, is still lacking. Based on previous findings that the same developmental program can be invoked by different cells on the lineage tree to produce highly similar subtrees, we designedDevelopmental CellLineageTreeAlignment (DELTA), an algorithm that exhaustively searches for lineage trees with phenotypic resemblance in lineal organization of terminal cells, meanwhile resolving detailed correspondence between individual cells. Using simulated and nematode lineage trees, we demonstrated DELTA’s capability of revealing similarities of developmental programs by lineal resemblances. Moreover, DELTA successfully identifies gene deletion-triggered homeotic cell fate transformations, reveals functional relationship between mutants by quantifying their lineal similarities, and finds the evolutionary correspondence between cell types defined non-uniformly for different species. DELTA establishes novel foundation for comparative study of lineage trees, much like sequence alignment algorithm for biological sequences, and along with the increase of lineage tree data, will likely bring unique insights for the myriads of important questions surrounding cell lineage trees.


2020 ◽  
Author(s):  
Liana Fasching ◽  
Yeongjun Jang ◽  
Simone Tomasi ◽  
Jeremy Schreiner ◽  
Livia Tomasini ◽  
...  

AbstractPost-zygotic mosaic mutations can be used to track cell lineages in humans. By using cell cloning and induced pluripotent cell lines, we analyzed early cell lineages in two living individuals (a patient and a control), and a postmortem human specimen. Of ten reconstructed post-zygotic divisions, none resulted in balanced contributions of daughter lineages to tissues. In both living individuals one of two lineages from the first cleavage was dominant across tissues, with 90% frequency in blood. We propose that the efficiency of DNA repair contributes to lineage imbalance. Allocation of lineages in postmortem brain correlated with anterior-posterior axis, associating lineage history with cell fate choices in embryos. Recurrence of germline variants as mosaic suggested that certain loci may be particularly susceptible to mutagenesis. We establish a minimally invasive framework for defining cell lineages in any living individual, which paves the way for studying their relevance in health and disease.


Science ◽  
2021 ◽  
Vol 371 (6535) ◽  
pp. 1245-1248
Author(s):  
Liana Fasching ◽  
Yeongjun Jang ◽  
Simone Tomasi ◽  
Jeremy Schreiner ◽  
Livia Tomasini ◽  
...  

Mosaic mutations can be used to track cell lineages in humans. We used cell cloning to analyze embryonic cell lineages in two living individuals and a postmortem human specimen. Of 10 reconstructed postzygotic divisions, none resulted in balanced contributions of daughter lineages to tissues. In both living individuals, one of two lineages from the first cleavage was dominant across tissues, with 90% frequency in blood. We propose that the efficiency of DNA repair contributes to lineage imbalance. Allocation of lineages in postmortem brain correlated with anterior-posterior axis, associating lineage history with cell fate choices in embryos. We establish a minimally invasive framework for defining cell lineages in any living individual, which paves the way for studying their relevance in health and disease.


Development ◽  
1992 ◽  
Vol 116 (4) ◽  
pp. 943-952 ◽  
Author(s):  
X. Cui ◽  
C.Q. Doe

Cell diversity in the Drosophila central nervous system (CNS) is primarily generated by the invariant lineage of neural precursors called neuroblasts. We used an enhancer trap screen to identify the ming gene, which is transiently expressed in a subset of neuroblasts at reproducible points in their cell lineage (i.e. in neuroblast ‘sublineages’), suggesting that neuroblast identity can be altered during its cell lineage. ming encodes a predicted zinc finger protein and loss of ming function results in precise alterations in CNS gene expression, defects in axonogenesis and embryonic lethality. We propose that ming controls cell fate within neuroblast cell lineages.


2004 ◽  
Vol 24 (19) ◽  
pp. 8428-8436 ◽  
Author(s):  
Heon-Jin Lee ◽  
Wolfgang Göring ◽  
Matthias Ochs ◽  
Christian Mühlfeld ◽  
Gerd Steding ◽  
...  

ABSTRACT The Sox genes define a family of transcription factors that play a key role in the determination of cell fate during development. The preferential expression of the Sox15 in the myogenic precursor cells led us to suggest that the Sox15 is involved in the specification of myogenic cell lineages or in the regulation of the fusion of myoblasts to form myotubes during the development and regeneration of skeletal muscle. To identify the physiological function of Sox15 in mice, we disrupted the Sox15 by homologous recombination in mice. Sox15-deficient mice were born at expected ratios, were healthy and fertile, and displayed normal long-term survival rates. Histological analysis revealed the normal ultrastructure of myofibers and the presence of comparable amounts of satellite cells in the skeletal muscles of Sox15−/− animals compared to wild-type animals. These results exclude the role of Sox15 in the development of satellite cells. However, cultured Sox15−/− myoblasts displayed a marked delay in differentiation potential in vitro. Moreover, skeletal muscle regeneration in Sox15−/− mice was attenuated after application of a crush injury. These results suggest a requirement for Sox15 in the myogenic program. Expression analyses of the early myogenic regulated factors MyoD and Myf5 showed the downregulation of the MyoD and upregulation of the Myf5 in Sox15−/− myoblasts. These results show an increased proportion of the Myf5-positive cells and suggest a role for Sox15 in determining the early myogenic cell lineages during skeletal muscle development.


2018 ◽  
Author(s):  
Irepan Salvador-Martínez ◽  
Marco Grillo ◽  
Michalis Averof ◽  
Maximilian J. Telford

Cell lineages provide the framework for understanding how multicellular organisms are built and how cell fates are decided during development. Describing cell lineages in most organisms is challenging, given the number of cells involved; even a fruit fly larva has ~50,000 cells and a small mammal has more than 1 billion cells. Recently, the idea of using CRISPR to induce mutations during development as heritable markers for lineage reconstruction has been proposed and trialled by several groups. While an attractive idea, its practical value depends on the accuracy of the cell lineages that can be generated by this method. Here, we use computer simulations to estimate the performance of this approach under different conditions. Our simulations incorporate empirical data on CRISPR-induced mutation frequencies in Drosophila. We show significant impacts from multiple biological and technical parameters - variable cell division rates, skewed mutational outcomes, target dropouts and different mutation sequencing strategies. Our approach reveals the limitations of recently published CRISPR recorders, and indicates how future implementations can be optimised to produce accurate cell lineages.


2014 ◽  
Vol 11 (93) ◽  
pp. 20130815 ◽  
Author(s):  
Bevan L. Cheeseman ◽  
Dongcheng Zhang ◽  
Benjamin J. Binder ◽  
Donald F. Newgreen ◽  
Kerry A. Landman

Cell lineage tracing is a powerful tool for understanding how proliferation and differentiation of individual cells contribute to population behaviour. In the developing enteric nervous system (ENS), enteric neural crest (ENC) cells move and undergo massive population expansion by cell division within self-growing mesenchymal tissue. We show that single ENC cells labelled to follow clonality in the intestine reveal extraordinary and unpredictable variation in number and position of descendant cells, even though ENS development is highly predictable at the population level. We use an agent-based model to simulate ENC colonization and obtain agent lineage tracing data, which we analyse using econometric data analysis tools. In all realizations, a small proportion of identical initial agents accounts for a substantial proportion of the total final agent population. We term these individuals superstars. Their existence is consistent across individual realizations and is robust to changes in model parameters. This inequality of outcome is amplified at elevated proliferation rate. The experiments and model suggest that stochastic competition for resources is an important concept when understanding biological processes which feature high levels of cell proliferation. The results have implications for cell-fate processes in the ENS.


2020 ◽  
Author(s):  
Ivan Croydon Veleslavov ◽  
Michael P.H. Stumpf

AbstractSingle cell transcriptomics has laid bare the heterogeneity of apparently identical cells at the level of gene expression. For many cell-types we now know that there is variability in the abundance of many transcripts, and that average transcript abun-dance or average gene expression can be a unhelpful concept. A range of clustering and other classification methods have been proposed which use the signal in single cell data to classify, that is assign cell types, to cells based on their transcriptomic states. In many cases, however, we would like to have not just a classifier, but also a set of interpretable rules by which this classification occurs. Here we develop and demonstrate the interpretive power of one such approach, which sets out to establish a biologically interpretable classification scheme. In particular we are interested in capturing the chain of regulatory events that drive cell-fate decision making across a lineage tree or lineage sequence. We find that suitably defined decision trees can help to resolve gene regulatory programs involved in shaping lineage trees. Our approach combines predictive power with interpretabilty and can extract logical rules from single cell data.


2019 ◽  
Author(s):  
Jean Feng ◽  
William S DeWitt ◽  
Aaron McKenna ◽  
Noah Simon ◽  
Amy Willis ◽  
...  

AbstractCRISPR technology has enabled large-scale cell lineage tracing for complex multicellular organisms by mutating synthetic genomic barcodes during organismal development. However, these sophisticated biological tools currently use ad-hoc and outmoded computational methods to reconstruct the cell lineage tree from the mutated barcodes. Because these methods are agnostic to the biological mechanism, they are unable to take full advantage of the data’s structure. We propose a statistical model for the mutation process and develop a procedure to estimate the tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. In contrast to existing techniques, our method estimates time along each branch, rather than number of mutation events, thus providing a detailed account of tissue-type differentiation. Via simulations, we demonstrate that our method is substantially more accurate than existing approaches. Our reconstructed trees also better recapitulate known aspects of zebrafish development and reproduce similar results across fish replicates.


2020 ◽  
Author(s):  
Irepan Salvador-Martínez ◽  
Marco Grillo ◽  
Michalis Averof ◽  
Maximilian J Telford

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. Visualising, interpreting and exploring these complex data in an intuitive manner requires the development of new tools. Here we present CeLaVi, a web-based visualisation tool that allows users to navigate and interact with a representation of cell lineages, whilst simultaneously visualising 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, tissue layer) 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.


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