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Results for “Lineage tree”

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At least 19 recordsLinked to original sources

PubMed2026

LAML-Pro: joint maximum likelihood inference of cell genotypes and cell lineage trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (i) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (ii) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈25%-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is implemented in C++ and is available as both a command-line interface and as a Python library at: github.com/raphael-group/LAML-Pro.

Cell Lineage↗

PubMed2005

Genomic variability within an organism exposes its cell lineage tree.

What is the lineage relation among the cells of an organism? The answer is sought by developmental biology, immunology, stem cell research, brain research, and cancer research, yet complete cell lineage trees have been reconstructed only for simple organisms such as Caenorhabditis elegans. We discovered that somatic mutations accumulated during normal development of a higher organism implicitly encode its entire cell lineage tree with very high precision. Our mathematical analysis of known mutation rates in microsatellites (MSs) shows that the entire cell lineage tree of a human embryo, or a mouse, in which no cell is a descendent of more than 40 divisions, can be reconstructed from information on somatic MS mutations alone with no errors, with probability greater than 99.95%. Analyzing all approximately 1.5 million MSs of each cell of an organism may not be practical at present, but we also show that in a genetically unstable organism, analyzing only a few hundred MSs may suffice to reconstruct portions of its cell lineage tree. We demonstrate the utility of the approach by reconstructing cell lineage trees from DNA samples of a human cell line displaying MS instability. Our discovery and its associated procedure, which we have automated, may point the way to a future "Human Cell Lineage Project" that would aim to resolve fundamental open questions in biology and medicine by reconstructing ever larger portions of the human cell lineage tree.

Animals↗

PubMed2026

LAML-Pro: Joint Maximum Likelihood Inference of Cell Genotypes and Cell Lineage Trees.

MOTIVATION: Recent dynamic lineage tracing technologies use genome editing to induce heritable mutations, or edits, that accumulate across successive cell divisions. These edits are measured using single-cell sequencing or imaging, providing data to reconstruct cell lineages at single-cell resolution. Current computational approaches to infer cell lineage trees, or phylogenies, from these data perform two separate steps: (1) Identify each cell's edits (genotype) from the raw sequencing or imaging data; (2) Infer a cell lineage tree from the cell genotypes. However, genotyping cells is an inexact process and genotype errors can yield an inaccurate lineage tree. For example, using fluorescence based-imaging to measure edits results in a high fraction (≈ 25-50%) of uncertain or erroneous genotypes. RESULTS: We introduce Lineage Analysis via Maximum Likelihood with PRobabilistic Observations (LAML-Pro), an algorithm that jointly infers cell genotypes and a cell lineage tree. LAML-Pro is based on the Probabilistic Mixed-type Missing Observation (PMMO) model, which we derive to describe both the genome editing and genotype observation processes. LAML-Pro constructs lineage trees from thousands of cells in under an hour by leveraging the sparsity of transitions under the PMMO model. On simulated data, we demonstrate that LAML-Pro corrects genotype errors and infers substantially more accurate trees than existing methods which are vulnerable to genotype errors. Applied to data from two recent imaging-based lineage tracing systems, LAML-Pro reduces genotype errors by 5-fold and produces more spatially coherent lineage trees compared to existing methods. AVAILABILITY AND IMPLEMENTATION: LAML-Pro is freely available at: github.com/raphael-group/LAML-Pro.

Journal Article↗

PubMed2005

Immunoglobulin variable-region gene mutational lineage tree analysis: application to autoimmune diseases.

Lineage trees have frequently been drawn to illustrate diversification, via somatic hypermutation (SHM), of immunoglobulin variable-region (IGV) genes. In order to extract more information from IGV sequences, we developed a novel mathematical method for analyzing the graphical properties of IgV gene lineage trees, allowing quantification of the differences between the dynamics of SHM and antigen-driven selection in different lymphoid tissues, species, and disease situations. Here, we investigated trees generated from published IGV sequence data from B cell clones participating in autoimmune responses in patients with Myasthenia Gravis (MG), Rheumatoid Arthritis (RA), and Sjögren's Syndrome (SS). At present, as no standards exist for cell sampling and sequence extraction methods, data obtained by different research groups from two studies of the same disease often vary considerably. Nevertheless, based on comparisons of data groups within individual studies, we show here that lineage trees from different individual patients are often similar and can be grouped together, as can trees from two different tissues in the same patient, and even from IgG- and IgA-expressing B cell clones. Additionally, lineage trees from most studies reflect the chronic character of autoimmune diseases.

Arthritis, Rheumatoid↗

PubMed2026

Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT.

The advent of single-cell lineage-tracing technologies has enabled the simultaneous profiling of gene expression and lineage barcodes. However, accurate, high-resolution reconstruction of cell lineage trees remains challenging because most existing approaches treat these modalities separately and therefore fail to fully exploit their complementary information. Here we present BiLinT, a Bayesian framework that jointly models multimodal single-cell lineage-tracing data for lineage tree reconstruction. BiLinT integrates barcode evolution (a continuous-time Markov chain) with gene expression dynamics (an Ornstein-Uhlenbeck process) within a unified probabilistic model. Across synthetic and real data sets, BiLinT provides accurate lineage-tree reconstruction and reveals differentiation-associated clonal structure and developmental fate biases.

Journal Article↗

PubMed2004

Analysis of mutational lineage trees from sites of primary and secondary Ig gene diversification in rabbits and chickens.

Lineage trees of mutated rearranged Ig V region sequences in B lymphocyte clones often serve to qualitatively illustrate claims concerning the dynamics of affinity maturation. In this study, we use a novel method for analyzing lineage tree shapes, using terms from graph theory to quantify the differences between primary and secondary diversification in rabbits and chickens. In these species, Ig gene diversification starts with rearrangement of a single (in chicken) or a few (in rabbit) V(H) genes. Somatic hypermutation and gene conversion contribute to primary diversification in appendix of young rabbits or in bursa of Fabricius of embryonic and young chickens and to secondary diversification during immune responses in germinal centers (GCs). We find that, at least in rabbits, primary diversification appears to occur at a constant rate in the appendix, and the type of Ag-specific selection seen in splenic GCs is absent. This supports the view that a primary repertoire is being generated within the expanding clonally related B cells in appendix of young rabbits and emphasizes the important role that gut-associated lymphoid tissues may play in early development of mammalian immune repertoires. Additionally, the data indicate a higher rate of hypermutation in rabbit and chicken GCs, such that the balance between hypermutation and selection tends more toward mutation and less toward selection in rabbit and chicken compared with murine GCs.

Algorithms↗

PubMed2002

The dynamics of germinal centre selection as measured by graph-theoretical analysis of mutational lineage trees.

We have developed a rigorous graph-theoretical algorithm for quantifying the shape properties of mutational lineage trees. We show that information about the dynamics of hypermutation and antigen-driven clonal selection during the humoral immune response is contained in the shape of mutational lineage trees deduced from the responding clones. Age and tissue related differences in the selection process can be studied using this method. Thus, tree shape analysis can be used as a means of elucidating humoral immune response dynamics in various situations.

Algorithms↗

PubMed2006

Quantitative analysis of clonal bone marrow CD19+ B cells: use of B cell lineage trees to delineate their role in the pathogenesis of light chain amyloidosis.

Light chain amyloidosis (AL) is a bone marrow (BM) plasma cell neoplasia with systemic deposition of Ig light chain amyloid fibrils. Here, we report the identification of clonal CD19 B cells in the BM and the use of a novel mathematical algorithm to generate B cell lineage trees of the clonal CD19 B cells and CD138 plasma cells from the BM of AL patients to delineate the relationship between these two clonal populations. The CD19+ clonal B cells in the BM of AL patients related to the clonal plasma cells represent a pre-plasma cell precursor population. The B cell lineage trees from AL patients also show significant differences in clonal diversification and antigenic selection compared to clones from normal, healthy controls. These data provide a robust example of the use of graphical quantification methods in delineating the role of neoplastic precursors in the pathogenesis of hematopoietic malignancies.

Algorithms↗

PubMed2006

Mutation parameters from DNA sequence data using graph theoretic measures on lineage trees.

MOTIVATION: B cells responding to antigenic stimulation can fine-tune their binding properties through a process of affinity maturation composed of somatic hypermutation, affinity-selection and clonal expansion. The mutation rate of the B cell receptor DNA sequence, and the effect of these mutations on affinity and specificity, are of critical importance for understanding immune and autoimmune processes. Unbiased estimates of these properties are currently lacking due to the short time-scales involved and the small numbers of sequences available. RESULTS: We have developed a bioinformatic method based on a maximum likelihood analysis of phylogenetic lineage trees to estimate the parameters of a B cell clonal expansion model, which includes somatic hypermutation with the possibility of lethal mutations. Lineage trees are created from clonally related B cell receptor DNA sequences. Important links between tree shapes and underlying model parameters are identified using mutual information. Parameters are estimated using a likelihood function based on the joint distribution of several tree shapes, without requiring a priori knowledge of the number of generations in the clone (which is not available for rapidly dividing populations in vivo). A systematic validation on synthetic trees produced by a mutating birth-death process simulation shows that our estimates are precise and robust to several underlying assumptions. These methods are applied to experimental data from autoimmune mice to demonstrate the existence of hypermutating B cells in an unexpected location in the spleen.

Algorithms↗

PubMed2002

Age- and tissue-specific differences in human germinal center B cell selection revealed by analysis of IgVH gene hypermutation and lineage trees.

The elderly produce increased levels of antibodies to autologous antigens and are less able to make high-affinity antibodies to foreign antigens. Ig gene hypermutation is integral to the affinity maturation process but previous studies of hypermutation with age have yielded conflicting results. The cells studied have represented post-germinal center (GC) populations and, therefore, the results may be complicated by possible differences in activation history. We studied Ig genes from GC B cells to elucidate which factors in the affinity maturation process change with age. Age-related changes in the pattern of hypermutation were seen, although the analysis of variable region heavy chain (VH) genes and their lineage trees shows that an alteration in the mechanism of somatic hypermutation is unlikely. The changes are due to founder cell effects and/or the process of selection. Striking tissue-specific differences were seen. All measurements indicated that selection of Ig genes may decrease in Peyer's patch GC but increase in splenic GC with age. These tissue-specific differences highlight the importance of considering the activation and effector sites when studying immune senescence.

Aging↗

PubMed1998

Stem cells in the embryonic cerebral cortex: their role in histogenesis and patterning.

The cytoarchitectural simplicity of the cerebral cortex makes it an attractive system to study central nervous system (CNS) histogenesis--the process whereby diverse cells are generated in the right numbers at the appropriate place and time. Recently, multipotent stem cells have been implicated in this process, as progenitor cells for diverse types of cortical neurons and glia. Continuous analysis of stem cell clone development reveals stereotyped division patterns within their lineage trees, highly reminiscent of neural lineage trees in arthropods and Caenorhabditis elegans. Given that these division patterns play a critical part in generating diverse neural types in invertebrates, we speculate that they play a similar role in the cortex. Because stereotyped lineage trees can be observed from cells growing at clonal density, cell-intrinsic factors are likely to have a key role in stem cell behavior. Cortical stem cells also respond to environmental signals to alter the types of cells they generate, providing the means for feedback regulation on the germinal zone. Evidence is accumulating that cortical stem cells, influenced by intrinsic programs and environmental signals, actually change with development-for example, by reducing the number and types of neurons they produce. Age-related changes in the stem cell population may have a critical role in orchestrating development; whether these cells truly self-renew is a point of discussion. In summary, we propose that cortical stem cells are the focus of regulatory mechanisms central to the development of the cortical cytoarchitecture.

Animals↗

PubMed2006

True monolayer cell culture in a confined 3D microenvironment enables lineage informatics.

BACKGROUND: There is a need for methods to (1) track cells continuously to generate lineage trees; (2) culture cells in in vivo-like microenvironments; and (3) measure many biological parameters simultaneously and noninvasively. Herein, we present a novel imaging culture chamber that facilitates "lineage informatics," a lineage-centric approach to cytomics. METHODS: We cultured cells in a confined monolayer using a novel "gap chamber" that produces images with confocal-like qualities using standard DIC microscopy. Lineage and other cytometric data were semiautomatically extracted from image sets of neural stem and progenitor cells and analyzed using lineage informatics. RESULTS: Cells imaged in the chamber every 3 min could be tracked for at least 6 generations allowing for the construction of extensive lineage trees with multiparameter data sets at hundreds of time points for each cell. The lineage informatics approach reveals relationships between lineage, phenotype, and microenvironment. Mass transfer characteristics and 3D geometry make the chamber more in vivo-like than traditional culture systems. CONCLUSIONS: The gap chamber allows cells to be cultured, imaged, and tracked in true monolayers permitting detailed informatics analysis of cell lineage, phenotype, and fate determinants. The chamber is biomimetic and straightforward to build and use, and should find many applications in long-term cell imaging.

Animals↗

PubMed2025

LINNAEUS: Simultaneous Single-Cell Lineage Tracing and Cell Type Identification.

A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging. Here we present LINNAEUS (Spanjaard et al, Nat Biotechnol 36:469-473. https://doi.org/10.1038/nbt.4124 , 2018; Hu et al, Nat Genet 54:1227-1237. https://doi.org/10.1038/s41588-022-01129-5 , 2022) (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, LINNAEUS can be used to reconstruct organism-wide single-cell lineage trees. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Single-Cell Analysis↗

PubMed2006

Novel analysis of clonal diversification in blood B cell and bone marrow plasma cell clones in immunoglobulin light chain amyloidosis.

Immunoglobulin light chain amyloidosis (AL) is characterized by a limited clonal expansion of plasma cells and amyloid formation. Here, we report restriction in the diversity of VL gene usage with a dominance of clonally related B cells in the peripheral blood (PB) isotype-specific repertoire of AL patients. A rigorous quantification of lineage trees reveals presence of intraclonal variations in the PB clones compared to the bone marrow (BM) clones, which suggests a common precursor that is still subject to somatic mutation. When compared to normal BM and PB B cells, AL clones showed significant but incomplete impairment of antigenic selection, which could not be detected by conventional R and S mutation analysis. Therefore, graphical analysis of B cell lineage trees and mathematical quantification of tree properties provide novel insights into the process of B cell clonal evolution in AL.

Algorithms↗

PubMed2025

Tracking Somatic Mutations for Lineage Reconstruction.

The human genome is composed of distinct genomic regions that are susceptible to various types of somatic mutations. Among these, Short Tandem Repeats (STRs) stand out as the most mutable genetic elements. STRs are short repetitive polymorphic sequences, predominantly situated within noncoding sectors of the genome. The intrinsic repetition characterizing these sequences makes them highly mutable in vivo. Consequently, this characteristic provides the chance to unravel the natural developmental history of human viable cells retrospectively. However, STRs also introduce stutter noise in vitro amplification, which makes their analysis challenging. Here we describe our integrated biochemical-computational platform for single-cell lineage analysis. It consists of a pipeline whose inputs are single cells and whose output is a lineage tree of input cells.

Humans↗

PubMed2025

Paired Single-Cell Transcriptome and DNA Barcode Detection in Zebrafish Using ScarTrace.

ScarTrace is a CRISPR/Cas9-based genetic lineage tracing method that allows for uniquely barcoding the DNA of single cells at a target GFP sequence during developing zebrafish embryos. Single cells from barcoded adult zebrafish can be isolated from various tissues (e.g., marrow, brain, eyes, fins), and their transcriptome and barcode sequences are captured by single-cell cDNA amplification and genomic DNA nested PCR, respectively. Computationally, cell type and barcode identification permit clone tracing and lineage tree reconstruction of tissues to unravel fate decisions during embryogenesis.

Animals↗

PubMed2006

Automated cell lineage tracing in Caenorhabditis elegans.

The invariant cell lineage and cell fate of Caenorhabditis elegans provide a unique opportunity to decode the molecular mechanisms of animal development. To exploit this opportunity, we have developed a system for automated cell lineage tracing during C. elegans embryogenesis, based on 3D, time-lapse imaging and automated image analysis. Using ubiquitously expressed histone-GFP fusion protein to label cells/nuclei and a confocal microscope, the imaging protocol captures embryogenesis at high spatial (31 planes at 1 microm apart) and temporal (every minute) resolution without apparent effects on development. A set of image analysis algorithms then automatically recognizes cells at each time point, tracks cell movements, divisions and deaths over time and assigns cell identities based on the canonical naming scheme. Starting from the four-cell stage (or earlier), our software, named starrynite, can trace the lineage up to the 350-cell stage in 25 min on a desktop computer. The few errors of automated lineaging can then be corrected in a few hours with a graphic interface that allows easy navigation of the images and the reported lineage tree. The system can be used to characterize lineage phenotypes of genes and/or extended to determine gene expression patterns in a living embryo at the single-cell level. We envision that this automation will make it practical to systematically decipher the developmental genes and pathways encoded in the genome of C. elegans.

Animals↗

PubMed2025

Cloning and validating systems for high throughput molecular recording.

Molecular recording technologies record and store information about cellular history. Lineage tracing is one form of molecular recording and produces information describing cellular trajectories during mammalian development, differentiation and maintenance of adult stem cell niches, and tumor evolution. Our molecular recorder technology utilizes CRISPR-Cas9 barcode editing to generate mutations in genomically integrated, engineered DNA cassettes, which are read out by single-cell RNA sequencing and used to produce high-resolution lineage trees. Here, we describe optimized cloning and validation procedures to construct the molecular recorder lineage tracing system. We include information on considerations of technology design, cloning procedures, the generation of lineage tracing cell lines, and time course experiments to assess their performance.

Cloning, Molecular↗