TiDeTree: A Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data

Seidel, Sophie; Stadler, Tanja

doi:10.1101/2022.02.14.480422

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…com/seidels/tidetree-material. All data underlying the analyses are available from the Dryad Digital Repository: https://doi.org/ 10.5061/dryad.qz612jmk7 [31]. Supplementary material is available online [32].…”

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TiDeTree: a Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data

Seidel

Stadler

2022

Proc. R. Soc. B.

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The development of organisms and tissues is dictated by an elaborate balance between cell division, apoptosis and differentiation: the cell population dynamics. To quantify these dynamics, we propose a phylodynamic inference approach based on single-cell lineage recorder data. We developed a Bayesian phylogenetic framework—time-scaled developmental trees (TiDeTree)—that uses lineage recorder data to estimate time-scaled single-cell trees. By implementing TiDeTree within BEAST 2, we enable joint inference of the time-scaled trees and the cell population dynamics. We validated TiDeTree using simulations and showed that performance further improves when including multiple independent sources of information into the inference, such as frequencies of editing outcomes or experimental replicates. We benchmarked TiDeTree against state-of-the-art methods and show comparable performance in terms of tree topology, plus direct assessment of uncertainty and co-estimation of additional parameters. To demonstrate TiDeTree’s use in practice, we analysed a public dataset containing lineage data from approximately 100 stem cell colonies. We estimated a time-scaled phylogeny for each colony; as well as the cell division and apoptosis rates underlying the growth dynamics of all colonies. We envision that TiDeTree will find broad application in the analysis of single-cell lineage tracing data, which will improve our understanding of cellular processes during development.

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TiDeTree: a Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data

Seidel

Stadler

2022

Proc. R. Soc. B.

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Mapping lineage-traced cells across time points with moslin

Lange

Piran

Klein

et al. 2023

Preprint

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Simultaneous profiling of single-cell gene expression and lineage history holds enormous potential for studying cellular decision-making beyond simpler pseudotime-based approaches. However, it is currently unclear how lineage and gene expression information across experimental time points can be combined in destructive experiments, which is particularly challenging for in-vivo systems. Here we present moslin, a Fused Gromov-Wasserstein-based model to couple matching cellular profiles across time points. In contrast to existing methods, moslin leverages both intra-individual lineage relations and inter-individual gene expression similarity. We demonstrate on simulated and real data that moslin outperforms state-of-the-art approaches that use either one or both data modalities, even when the lineage information is noisy. On C. elegans embryonic development, we show how moslin, combined with trajectory inference methods, predicts fate probabilities and putative decision driver genes. Finally, we use moslin to delineate lineage relationships among transiently activated fibroblast states during zebrafish heart regeneration. We anticipate moslin to play a crucial role in deciphering complex state change trajectories from lineage-traced single-cell data.

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VIACOT: a web server for interactive visualization and comparison of cell lineage trees

Yang,

Li,

Zhang

et al. 2024

Preprint

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The cell lineage tree (CLT) records some or all cell division events during the development of multicellular organisms, as well as the functional characteristics of terminal cells, and is therefore commonly considered one of the most fundamental types of biological data. The combining of single-cell RNA sequencing with DNA barcoding has recently led to the rapid accumulation of CLT datasets. Unlike biological sequence data, web-based analysis and visualization tools for CLT data are currently lacking. In order to facilitate studies surrounding CLT, we built a webserver named VIACOT (Visualize Interactively And COmpare Trees). VIACOT is not only a comprehensive repository of published CLT datasets, but also a web tool that enables three preliminary analyses of preprocessed CLT datasets, including. (i) interactive visualization of CLT allowing fine-grain or coarse-grain exploration. (ii) calculations of correlations among single-cell metrics, such as gene expression levels, either using the original metrics or their phylogenetic independent contrasts. (iii) comparison among different CLTs to identify differences in cell fate. We anticipate that VIACOT will be a useful tool for exploration and biological interpretation of CLTs.

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TiDeTree: A Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data

Cited by 4 publications

References 34 publications

TiDeTree: a Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data

TiDeTree: a Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data

Mapping lineage-traced cells across time points with moslin

VIACOT: a web server for interactive visualization and comparison of cell lineage trees

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