Biology subjects

Zwaans, A.

Publications and source records attributed to Zwaans, A..

5 recordsLinked to original sources

bioRxiv2026

Bayesian phylogenetics with incomplete lineage tracing data

Single-cell lineage tracing is rapidly emerging as a powerful method to track the dynamics of cell populations through time. Specifically, CRISPR-Cas9-based dynamic lineage tracing methods generate high-resolution data with the potential to investigate increasingly complex biological processes. However, single-cell readouts are inherently lossy, resulting in significant sparsity in lineage barcode alignments. Here, we present a mechanistic model of barcode loss implemented in the Bayesian framework BEAST 2, enabling phylogenetic inference from sparse datasets without the need for data filtering. Through simulations, we find that incorporating a mechanistic model of loss improves accuracy across all estimates in phylogenetic analyses by enabling previously unusable data to contribute to the inference. We further demonstrate the model's utility across two datasets: one from a monoclonal cell culture and another from murine gastruloids. Consistent with our simulation findings, applying our model to these datasets recovers higher growth rates than workflows relying on standard data filtering, confirming that such filtering can lead to an underestimation of population expansion. Crucially, we demonstrate that our framework enables inference even from datasets whose sparsity previously prevented analysis, providing a robust foundation for extracting biological insights from lineage tracing experiments.

bioinformatics↗

bioRxiv2025

Single-cell phylodynamics reveal rapid late-stage colorectal cancer expansions

Single-cell whole-genome sequencing of 335 cells from seven colorectal cancers, coupled with Bayesian phylodynamic modeling, revealed tumors often originate decades before diagnosis, remain indolent, and expand rapidly within two years. Strong spatial structuring with minimal interregional mixing was observed, while substitution rates varied widely and were decoupled from growth. Our findings highlight extended evolutionary stasis and sudden expansion, informing strategies for early detection and intervention.

evolutionary biology↗

bioRxiv2024

Sciphy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data.

CRISPR-based lineage tracing offers a promising avenue to decipher single cell lineage trees, especially in organisms that are challenging for microscopy. A recent advancement in this domain is lineage tracing based on sequential genome editing, which not only records genetic edits but also the order in which they occur. To capitalize on this enriched data, we introduce SciPhy, a simulation and inference tool integrated within the BEAST 2 framework. SciPhy utilizes a Bayesian phylogenetic approach to estimate time-scaled phylogenies and cell population parameters. After validating SciPhy using simulations, we apply it to two lineage tracing datasets for which we estimate time-scaled trees together with cell proliferation rates. Using simulated and real lineage tracing data obtained from a monoclonal culture of HEK293T cells, we compare SciPhy to other lineage reconstruction methods, and find that SciPhy consistently constructs distinct, and more accurate lineage trees. In particular, for HEK293T cells, SciPhy trees stand out for their later estimated cell division times. In addition, SciPhy reports uncertainty as well as proliferation rates, neither of which are available within a UPGMA analysis. Our second example applies SciPhy to the study of murine gastruloids, and showcases the use of complex models of time-varying population growth to capture realistic aspects of this in-vitro model of early mammalian development. Together, these examples showcase the application of advanced phylogenetic and phylodynamic tools to explore and quantify cell lineage trees, laying the groundwork for enhanced and confident analyses to decode the complexities of biological development in multicellular organisms. SciPhys codebase is publicly available at https://github.com/azwaans/SciPhy.

developmental biology↗

bioRxiv2024

Bayesian phylodynamics of early vertebrate development in BEAST 2

Analysing single-cell lineage relationships of an organism is crucial towards understanding the fundamental cellular dynamics that drive development. CRISPR-based dynamic lineage tracing relies on recent advances in genome editing and sequencing technologies to generate inheritable, evolving genetic barcode sequences which enable reconstruction of such cell lineage trees, also referred to as phylogenetic trees. Recent work generated custom computational strategies to produce robust tree estimates from such data. We further capitalise on these advancements and introduce GABI (GESTALT Analysis using Bayesian Inference), which extends the analysis of GESTALT (Genome Editing of Synthetic Target Arrays for Lineage Tracing) data to a fully integrated Bayesian phylogenetic inference frame-work in the software BEAST 2. This implementation allows to represent the uncertainty in reconstructed tree reconstruction and enables their scaling in absolute time. Furthermore, based on such time-scaled lineage trees, the underlying processes of growth, differentiation and apoptosis are quantified through so-called phylodynamic inference, typically relying on a birthdeath or coalescent model. After validating the implementation, we demonstrate that the methodology results in robust estimates of lineage trees and growth dynamics characteristics of early zebrafish Danio rerio development. GABIs codebase is publicly available at https://github.com/azwaans/GABI.

bioinformatics↗

bioRxiv2020

A skyline birth-death process for inferring the population size from a reconstructed tree with occurrences

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, population size through time for clades of interest, based on molecular sequence data. In the fields of epidemiology and macroevolution these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of "total-evidence" analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, i.e. lacking morphological and molecular data, which we call occurrences, provide invaluable information to reconstruct past population sizes. Here, we present new methodological developments around the Fossilized Birth-Death Process enabling us to (i) efficiently incorporate occurrence data while remaining computationally tractable and scalable; (ii) consider piecewise-constant birth, death and sampling rates; and (iii) reconstruct past population sizes, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical datasets stemming from the fields of epidemiology and macroevolution. In epidemiology, we apply our model to the Covid-19 outbreak on the Diamond Princess ship. We infer the total prevalence throughout the outbreak, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we present an empirical case study of cetaceans. We infer the diversity trajectory using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. Our case studies highlight that the advances we present allow us to further bridge the gap between between epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics.

evolutionary biology↗