Cell Lineage Trees Research Articles

Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling.

Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development, and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field. We utilize the predicted cell motion jointly with spatial cell features to define a maximum likelihood criterion to find inter-frame cell correspondences assuming Markov dependency. We formulate cell tracking and cell event detection as a graph partitioning problem. We propose a solution obtained by minimization of a global cost function defined over the set of all cell tracks. We construct a cell lineage tree that represents the cell tracks and cell events. Finally, we compute morphological, motility, and diffusivity measures and validate cell tracking against manually generated reference standards. The automated tracking method applied to reference segmentation maps produces an average tracking accuracy score ( TRA) of 99 percent, and the fully automated segmentation and tracking system produces an average TRA of 89 percent.

Publisher Correction: Entropic effects in cell lineage tree packings

In the version of this Letter originally published, the citations to equation (1) in Fig. 3 caption and the main text were incorrect; they should have been to equation (2). This has now been corrected.

Entropic effects in cell lineage tree packings.

Optimal packings [1, 2] of unconnected objects have been studied for centuries [3–6], but the packing principles of linked objects, such as topologically complex polymers [7, 8] or cell lineages [9, 10], are yet to be fully explored. Here, we identify and investigate a generic class of geometrically frustrated tree packing problems, arising during the initial stages of animal development when interconnected cells assemble within a convex enclosure [10]. Using a combination of 3D imaging, computational image analysis, and mathematical modelling, we study the tree packing problem in Drosophila egg chambers, where 16 germline cells are linked by cytoplasmic bridges to form a branched tree. Our imaging data reveal non-uniformly distributed tree packings, in agreement with predictions from energy-based computations. This departure from uniformity is entropic and affects cell organization during the first stages of the animal’s development. Considering mathematical models of increasing complexity, we investigate spherically confined tree packing problems on convex polyhedrons [11] that generalize Platonic and Archimedean solids. Our experimental and theoretical results provide a basis for understanding the principles that govern positional ordering in linked multicellular structures, with implications for tissue organization and dynamics [12, 13].

Cell tracking in vitro reveals that the extracellular matrix glycoprotein Tenascin-C modulates cell cycle length and differentiation in neural stem/progenitor cells of the developing mouse spinal cord.

ABSTRACTGeneration of astrocytes during the development of the mammalian spinal cord is poorly understood. Previously, we have shown that the glycoprotein of the extracellular matrix (ECM) tenascin-C (Tnc) modulates the expression territories of the patterning genes Nkx6.1 and Nkx2.2 in the developing ventral spinal cord, tunes the responsiveness of neural stem/progenitor cells towards the cytokines FGF2 and EGF and thereby promotes astrocyte maturation. In order to obtain further mechanistic insight into these processes, we have compared embryonic day-15 spinal cord neural progenitor cells (NPCs) from wild-type and Tnc knockout mice using continuous single-cell live imaging and cell lineage analysis in vitro. Tnc knockout cells displayed a significantly reduced rate of cell division both in response to FGF2 and EGF. When individual clones of dividing cells were investigated with regard to their cell lineage trees using the tTt tracking software, it appeared that the cell cycle length in response to growth factors was reduced in the knockout. Furthermore, when Tnc knockout NPCs were induced to differentiate by the removal of FGF2 and EGF glial differentiation was enhanced. We conclude that the constituent of the stem cell niche Tnc contributes to preserve stemness of NPCs.

On the journey from nematode to human, scientists dive by the zebrafish cell lineage tree

Three recent single-cell papers use novel CRISPR-Cas9-sgRNA genome editing methods to shed light on the zebrafish cell lineage tree.

Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics.

Flatworms of the species Schmidtea mediterranea are immortal-adult animals contain a large pool of pluripotent stem cells that continuously differentiate into all adult cell types. Therefore, single-cell transcriptome profiling of adult animals should reveal mature and progenitor cells. By combining perturbation experiments, gene expression analysis, a computational method that predicts future cell states from transcriptional changes, and a lineage reconstruction method, we placed all major cell types onto a single lineage tree that connects all cells to a single stem cell compartment. We characterized gene expression changes during differentiation and discovered cell types important for regeneration. Our results demonstrate the importance of single-cell transcriptome analysis for mapping and reconstructing fundamental processes of developmental and regenerative biology at high resolution.

Nongenetic origins of cell-to-cell variability in B lymphocyte proliferation

Rapid antibody production in response to invading pathogens requires the dramatic expansion of pathogen-derived antigen-specific B lymphocyte populations. Whether B cell population dynamics are based on stochastic competition between competing cell fates, as in the development of competence by the bacterium Bacillus subtilis, or on deterministic cell fate decisions that execute a predictable program, as during the development of the worm Caenorhabditis elegans, remains unclear. Here, we developed long-term live-cell microscopy of B cell population expansion and multiscale mechanistic computational modeling to characterize the role of molecular noise in determining phenotype heterogeneity. We show that the cell lineage trees underlying B cell population dynamics are mediated by a largely predictable decision-making process where the heterogeneity of cell proliferation and death decisions at any given timepoint largely derives from nongenetic heterogeneity in the founder cells. This means that contrary to previous models, only a minority of genetically identical founder cells contribute the majority to the population response. We computationally predict and experimentally confirm nongenetic molecular determinants that are predictive of founder cells' proliferative capacity. While founder cell heterogeneity may arise from different exposure histories, we show that it may also be due to the gradual accumulation of small amounts of intrinsic noise during the lineage differentiation process of hematopoietic stem cells to mature B cells. Our finding of the largely deterministic nature of B lymphocyte responses may provide opportunities for diagnostic and therapeutic development.

On the statistical analysis of single cell lineage trees

Stem cells play a central role in the regeneration and repair of multicellular organisms. However, it remains far from trivial to reliably identify them. Despite decades of work, current techniques to isolate hematopoietic stem cells (HSCs) based on cell-surface markers only result in 50% purity, i.e. half of the sorted cells are not stem cells when functionally tested. Modern microscopy techniques allow us to follow single cells and their progeny for up to weeks in vitro, while recording the cell fates and lifetime of each individual cell. This cell tracking generates so-called lineage trees. Here, we propose statistical techniques to determine if the initial cell in a lineage tree was a HSC. We apply these techniques to murine hematopoietic lineage trees, revealing that 18% of the trees in our HSC dataset display a unique signature, and this signature is compatible with these trees having started from a true stem cell. Assuming 50% purity of HSC empirical datasets, this corresponds to a 0.35 power of the test, and the type-1-error is estimated to be 0.047. In summary, this study shows that statistical analysis of lineage trees could improve the classification of cells, which is currently done based on bio-markers only. Our statistical techniques are not limited to mammalian stem cell biology. Any type of single cell lineage trees, be it from bacteria, single cell eukaryotes, or single cells in a multicellular organism can be investigated. We expect this to contribute to a better understanding of the molecules influencing cellular dynamics at the single cell level.

Collective Growth in a Small Cell Network

Theoretical studies suggest that many of the emergent properties associated with multicellular systems arise already in small networks [1, 2]. However, the number of experimental models that can be used to explore collective dynamics in well-defined cell networks is still very limited. Here we focus on collective cell behavior in the female germline cyst in Drosophila melanogaster, a stereotypically wired network of 16 cells that grows by ∼4 orders of magnitude with unequal distribution of volume among its constituents. We quantify multicellular growth with single-cell resolution and show that proximity to the oocyte, as defined on the network, is the principal factor that determines cell size; consequently, cells grow in groups. To rationalize this emergent pattern of cell sizes, wepropose a tractable mathematical model that depends on intercellular transport on a cell lineage tree. In addition to correctly predicting the divergent pattern of cell sizes, this model reveals allometric growth of cells within the network, an emergent property of this system and a feature commonly associated with differential growth on an organismal scale [3].

VDJServer – a web-accessible analysis portal for immune repertoire sequencing analysis

Abstract VDJServer is a comprehensive, web-accessible system for analysis of immune repertoire sequencing data. VDJServer provides a complete analysis workflow from pre-processing of sequence reads, to V(D)J assignment, to repertoire characterization and comparison. Recent enhancements in VDJServer include: --Automatic parallelization of analysis tools to handle very large data sets running on a high-performance supercomputer--Import and export subject and sample metadata. User-defined sample groups allows for sophisticated group analysis and comparison.--Extensive analysis functionality such as gene segment usage, CDR3 patterns, clonality, diversity measures, somatic mutation patterns, B cell lineage trees, and quantification of selection pressure. Analysis is performed for both samples and sample groups.--Interactive charting of analysis data provides exploratory visualization for ad-hoc comparison of samples and sample groups. Charts can be downloaded as image files for use in presentations and publications. All analysis data can be downloaded in standard TSV format for use with external tools.--Novel process workflow metadata that is automatically captured by VDJServer. Hiding the complexities of command line tools and their parameters, yet providing complete transparency of the analysis workflow for reproducibility. VDJServer allows users to upload antigen receptor repertoire sequences and execute a customizable workflow for all steps in the analysis. Data and analysis results can be privately shared with other users for collaborative projects. VDJServer is funded by the NIAID and is freely available.

Estimate of within clones and between clone selection suggests a multi-stage selection process in the production of high affinity B cell receptors.

Abstract During an immune response, B cells undergo a process of clonal expansion, somatic hypermutation of the immunoglobulin genes and affinity-dependent selection. Over a lifetime, each B cell may participate in multiple rounds of affinity maturation as part of different immune responses. We characterized these two time-scales for selection which are apparent in the structure of B cell lineage trees. The “trunk” of the tree consists of mutations that are shared across all members of a clone, and the branches form a “canopy” consisting of mutations that are shared by a subset of clone members. We show that the framework regions are subjected to a negative selection at all stages. However, CDR regions are affected to purifying and antigen-driven positive selection on the short term, which leads to a net positive selection in the long term. Overall, we found that long-term selection is strongly dependent on the heavy chain variable gene family. To the selection in the tree canopy, we propose a novel mixed lineage tree/sequence based method to detect within population selection as defined by the effect of mutations on the average number of offspring. Specifically, we propose to measure the log of the ratio between the number of leaves in lineage trees branches following synonymous and non-synonymous mutations (LONR), and show that selection in CDR region occur at a different stage.

Image analysis driven single-cell analytics for systems microbiology

BackgroundTime-lapse microscopy is an essential tool for capturing and correlating bacterial morphology and gene expression dynamics at single-cell resolution. However state-of-the-art computational methods are limited in terms of the complexity of cell movies that they can analyze and lack of automation. The proposed Bacterial image analysis driven Single Cell Analytics (BaSCA) computational pipeline addresses these limitations thus enabling high throughput systems microbiology.ResultsBaSCA can segment and track multiple bacterial colonies and single-cells, as they grow and divide over time (cell segmentation and lineage tree construction) to give rise to dense communities with thousands of interacting cells in the field of view. It combines advanced image processing and machine learning methods to deliver very accurate bacterial cell segmentation and tracking (F-measure over 95%) even when processing images of imperfect quality with several overcrowded colonies in the field of view. In addition, BaSCA extracts on the fly a plethora of single-cell properties, which get organized into a database summarizing the analysis of the cell movie. We present alternative ways to analyze and visually explore the spatiotemporal evolution of single-cell properties in order to understand trends and epigenetic effects across cell generations. The robustness of BaSCA is demonstrated across different imaging modalities and microscopy types.ConclusionsBaSCA can be used to analyze accurately and efficiently cell movies both at a high resolution (single-cell level) and at a large scale (communities with many dense colonies) as needed to shed light on e.g. how bacterial community effects and epigenetic information transfer play a role on important phenomena for human health, such as biofilm formation, persisters’ emergence etc. Moreover, it enables studying the role of single-cell stochasticity without losing sight of community effects that may drive it.

Parallelization and Validation of Algorithms for Zebrafish Cell Lineage Tree Reconstruction from Big 4D Image Data

Parallelization and Validation of Algorithms for Zebrafish Cell Lineage Tree Reconstruction from Big 4D Image Data

Cell Lineage Trees Bear Fruit

Two inference approaches harness the information present in cell lineage trees to better understand the dynamic transitions between cell states.

An integrated modelling framework from cells to organism based on a cohort of digital embryos.

We conducted a quantitative comparison of developing sea urchin embryos based on the analysis of five digital specimens obtained by automatic processing of in toto 3D+ time image data. These measurements served the reconstruction of a prototypical cell lineage tree able to predict the spatiotemporal cellular organisation of a normal sea urchin blastula. The reconstruction was achieved by designing and tuning a multi-level probabilistic model that reproduced embryo-level dynamics from a small number of statistical parameters characterising cell proliferation, cell surface area and cell volume evolution along the cell lineage. Our resulting artificial prototype was embedded in 3D space by biomechanical agent-based modelling and simulation, which allowed a systematic exploration and optimisation of free parameters to fit the experimental data and test biological hypotheses. The spherical monolayered blastula and the spatial arrangement of its different cell types appeared tightly constrained by cell stiffness, cell-adhesion parameters and blastocoel turgor pressure.

Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells

Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate theautoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positiveNanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design andvalidate model predictions using novel experimental data. STILT is available for download as an open source framework from http://www.imsb.ethz.ch/research/claassen/Software/stilt---stochastic-inference-on-lineage-trees.html.

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.

High-precision morphology: bifocal 4D-microscopy enables the comparison of detailed cell lineages of two chordate species separated for more than 525 million years.

BackgroundUnderstanding the evolution of divergent developmental trajectories requires detailed comparisons of embryologies at appropriate levels. Cell lineages, the accurate visualization of cleavage patterns, tissue fate restrictions, and morphogenetic movements that occur during the development of individual embryos are currently available for few disparate animal taxa, encumbering evolutionarily meaningful comparisons. Tunicates, considered to be close relatives of vertebrates, are marine invertebrates whose fossil record dates back to 525 million years ago. Life-history strategies across this subphylum are radically different, and include biphasic ascidians with free swimming larvae and a sessile adult stage, and the holoplanktonic larvaceans. Despite considerable progress, notably on the molecular level, the exact extent of evolutionary conservation and innovation during embryology remain obscure.ResultsHere, using the innovative technique of bifocal 4D-microscopy, we demonstrate exactly which characteristics in the cell lineages of the ascidian Phallusia mammillata and the larvacean Oikopleura dioica were conserved and which were altered during evolution. Our accurate cell lineage trees in combination with detailed three-dimensional representations clearly identify conserved correspondence in relative cell position, cell identity, and fate restriction in several lines from all prospective larval tissues. At the same time, we precisely pinpoint differences observable at all levels of development. These differences comprise fate restrictions, tissue types, complex morphogenetic movement patterns, numerous cases of heterochronous acceleration in the larvacean embryo, and differences in bilateral symmetry.ConclusionsOur results demonstrate in extraordinary detail the multitude of developmental levels amenable to evolutionary innovation, including subtle changes in the timing of fate restrictions as well as dramatic alterations in complex morphogenetic movements. We anticipate that the precise spatial and temporal cell lineage data will moreover serve as a high-precision guide to devise experimental investigations of other levels, such as molecular interactions between cells or changes in gene expression underlying the documented structural evolutionary changes. Finally, the quantitative amount of digital high-precision morphological data will enable and necessitate software-based similarity assessments as the basis of homology hypotheses.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-015-0218-1) contains supplementary material, which is available to authorized users.

Bayesian detection of embryonic gene expression onset in C. elegans

To study how a zygote develops into an embryo with different tissues, large-scale 4D confocal movies of C. elegans embryos have been produced recently by experimental biologists. However, the lack of principled statistical methods for the highly noisy data has hindered the comprehensive analysis of these data sets. We introduced a probabilistic change point model on the cell lineage tree to estimate the embryonic gene expression onset time. A Bayesian approach is used to fit the 4D confocal movies data to the model. Subsequent classification methods are used to decide a model selection threshold and further refine the expression onset time from the branch level to the specific cell time level. Extensive simulations have shown the high accuracy of our method. Its application on real data yields both previously known results and new findings.

Fast and scalable inference of multi-sample cancer lineages.

Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0647-8) contains supplementary material, which is available to authorized users.