Faculty Opinions recommendation of Genomic variability within an organism exposes its cell lineage tree.

Abstract Abstract 178 Introduction: Leukemic cells are heterogeneous in many ways, and specifically in replication rates. Dormancy and quiescence particularly among leukemia stem cells (LSCs) have been suggested to play a pivotal role in leukemia resistance to chemotherapy. This has been suggested in chronic leukemia, but never proven in acute leukemia, where most cancer cells divide frequently, and it is not known which subpopulation causes relapse. Cell lineage analysis of single leukemia cells can describe the variable kinetics of leukemia subpopulations by studying the evolutionary genetic changes between diagnosis and relapse in the same patient, and hence potentially pinpoint the relapse population already at diagnosis. In the current study a novel methodology was used in order to describe the evolutionary history of single leukemia cells. The reconstruction of phylogenetic trees from single cells can shed light of the relative number of replications of each cell (depth), and the diversity and heterogeneity of the tumor. Method: Phylogenetic analysis was applied to single cells from peripheral blood of two acute myeloid leukemia (AML) patients (n≂F40 cells from each patient), sampled at diagnosis and relapse. Leukemia and T cells were separated by FACS. T cells, which were not part of the malignant AML clone served for the validation of sorting. Single cells underwent whole genome amplification followed by PCR reactions amplifying 120 microsatellite (MS) loci, using a high throughput robotic and computer aided systems. As MSs accumulate genetic variation during replication, the phylogenetic tree of the malignancies can be reconstructed from the genetic changes in MSs between the cells and the application of a neighbor joining algorithm. The relative depth of cells was calculated from the genetic distance of each cell from the root of the tree, which was designated as the median value of all cells for a specific MS. Result: In the reconstructed cell lineage trees of both AML patients (L1 n=41 leukemia cells; L2 n=43 leukemia cells), cells at relapse were shallow compared to cells at diagnosis, implying that they originate from cells that divided rarely prior to relapse (p<10e-5, p<0.0001, L1 and L2 respectively) (Figure 1). Furthermore for patient L2 single LSCs, LIN- CD34+ CD38- CD90+ from diagnosis, were analyzed (n=21 LSCs), and were found to be shallower than the general population of leukemia cells at diagnosis (p=0.01), but deeper than leukemia cells at relapse (p=0.036) (Figure 1). T cells in both AML patients were clustered on a different branch of the lineage tree (L1 n=32, p<10-6; L2 n=29, p=7.4*10-6). Conclusion: A novel single cell phylogenetic approach applied to AML cells uncovered the role of dormancy and LSCs in AML relapse. As chemotherapy preferentially targets rapidly-dividing cells, dormant cells are positively selected to resist chemotherapy at least in some AML patients. In such cases, in order to prevent relapse, leukemia therapy must also target such rarely-dividing leukemia cells. We have further demonstrated that LSCs are genetically correlated to the rarely dividing cells at relapse. The ability of cell lineage analysis to identify rarely dividing cells and correlate them to LSCs and possibly to normal stem cells already at diagnosis may enable their characterization and hence the design of improved targeted and personalized therapy for leukemia and other types of cancer with similar relapse mechanisms. Furthermore, this evolutionary-based approach can also shed more light on the diversity of leukemia in a time scale and uncover other chemotherapy resistance mechanisms. Phylogenetic trees of leukemia single cells at diagnosis and relapse from 2 AML patients. a, L1: Left – reconstructed lineage tree of CD33+ CD4+ CD15- peripheral blood (PB) individual leukemia cells at diagnosis (full, n=14); and relapse (blank, n=27) for patient L1; The root was determined by calculation of the median signal of all cells. Right - comparison of median depth between cells at diagnosis (full) and relapse (blank), (p<10^-5). b, L2: Left – reconstructed lineage tree of CD117+ PB individual leukemia blast cells at diagnosis (full, n=33), LSCs LIN-CD34+ CD38- CD90+ (gray, n=21) and relapse(blank, n=10) for patient L2. Right - Comparison of median depth between cells at diagnosis (full) and relapse (blank), (p=0.0001). Y axis represents depth in arbitrary units (logarithmic scale). Disclosures: No relevant conflicts of interest to declare.

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Accurate and efficient cell lineage tree inference from noisy single cell data: the maximum likelihood perfect phylogeny approach

Bioinformatics ◽

10.1093/bioinformatics/btz676 ◽

2019 ◽

Author(s):

Yufeng Wu

Keyword(s):

Single Cell ◽

Cell Lineage ◽

Large Data ◽

Genomic Variation ◽

Supplementary Information ◽

Perfect Phylogeny ◽

Tree Inference ◽

Lineage Tree ◽

Infinite Sites Model ◽

Cell Data

Abstract Motivation Cells in an organism share a common evolutionary history, called cell lineage tree. Cell lineage tree can be inferred from single cell genotypes at genomic variation sites. Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. A key missing aspect is that real single cell data usually has non-uniform uncertainty in individual genotypes. Moreover, existing methods are often sampling based and can be very slow for large data. Results In this article, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data. Different from most existing approaches, ScisTree works with genotype probabilities of individual genotypes (which can be computed by existing single cell genotype callers). ScisTree assumes the infinite sites model. Given uncertain genotypes with individualized probabilities, ScisTree implements a fast heuristic for inferring cell lineage tree and calling the genotypes that allow the so-called perfect phylogeny and maximize the likelihood of the genotypes. Through simulation, we show that ScisTree performs well on the accuracy of inferred trees, and is much more efficient than existing methods. The efficiency of ScisTree enables new applications including imputation of the so-called doublets. Availability and implementation The program ScisTree is available for download at: https://github.com/yufengwudcs/ScisTree. Supplementary information Supplementary data are available at Bioinformatics online.

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Scelestial: fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm

10.1101/2021.05.24.445405 ◽

2021 ◽

Author(s):

Mohammad-Hadi Foroughmand-Araabi ◽

Sama Goliaei ◽

Alice Carolyn McHardy

Keyword(s):

Approximation Algorithm ◽

Single Cell ◽

Steiner Tree ◽

Missing Values ◽

Cell Lineage ◽

Error Rates ◽

Steiner Tree Problem ◽

Tree Reconstruction ◽

Tree Inference ◽

Lineage Tree

Single-cell genome sequencing provides a highly granular view of biological systems but is affected by high error rates, allelic amplification bias, and uneven genome coverage. This creates a need for data-specific computational methods, for purposes such as for cell lineage tree inference. The objective of cell lineage tree reconstruction is to infer the evolutionary process that generated a set of observed cell genomes. Lineage trees may enable a better understanding of tumor formation and growth, as well as of organ development for healthy body cells. We describe a method, Scelestial, for lineage tree reconstruction from single-cell data, which is based on an approximation algorithm for the Steiner tree problem and is a generalization of the neighbor-joining method. We adapt the algorithm to efficiently select a limited subset of potential sequences as internal nodes, in the presence of missing values, and to minimize cost by lineage tree-based missing value imputation. In a comparison against seven state-of-the-art single-cell lineage tree reconstruction algorithms - BitPhylogeny, OncoNEM, SCITE, SiFit, SASC, SCIPhI, and SiCloneFit - on simulated and real single-cell tumor samples, Scelestial performed best at reconstructing trees in terms of accuracy and run time. Scelestial has been implemented in C++. It is also available as an R package named RScelestial.

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Estimation of cell lineage trees by maximum-likelihood phylogenetics

10.1101/595215 ◽

2019 ◽

Cited By ~ 5

Author(s):

Jean Feng ◽

William S DeWitt ◽

Aaron McKenna ◽

Noah Simon ◽

Amy Willis ◽

...

Keyword(s):

Maximum Likelihood ◽

Large Scale ◽

Ad Hoc ◽

Cell Lineage ◽

Likelihood Estimation ◽

Lineage Tracing ◽

Tissue Type ◽

Lineage Tree ◽

Organismal Development ◽

Multicellular Organisms

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.

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Maps of variability in cell lineage trees

10.1101/267450 ◽

2018 ◽

Cited By ~ 1

Author(s):

Damien G. Hicks ◽

Terence P. Speed ◽

Mohammed Yassin ◽

Sarah M. Russell

Keyword(s):

Cell Fate ◽

Binary Tree ◽

Cell Lineage ◽

Population Level ◽

Differentiation Potential ◽

Cell Lineages ◽

Lineage Tree ◽

Founder Cells ◽

Multicellular Organisms ◽

Lineage Trees

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.

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CeLaVi: An Interactive Cell Lineage Visualisation Tool

10.1101/2020.12.14.422765 ◽

2020 ◽

Author(s):

Irepan Salvador-Martínez ◽

Marco Grillo ◽

Michalis Averof ◽

Maximilian J Telford

Keyword(s):

Spatial Distribution ◽

Cell Lineage ◽

Data Sets ◽

Complex Data ◽

Cell Lineages ◽

Cell Divisions ◽

Visualisation Tool ◽

Cell Clones ◽

Lineage Tree ◽

A Cell

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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