Biology subjects

Seidel, S.

Publications and source records attributed to Seidel, S..

13 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↗

bioRxiv2026

In vivo reconstruction of the cell lineage history of a developing mouse with DNA Typewriter, from zygote to late organogenesis

The complete cell lineage of C. elegans, mapped over four decades ago, was tractable because the animal is small, transparent, and lineage invariant1. Most animals are none of these, having orders of magnitude more cells, being opaque, and developing with substantial stochasticity2. Since 2016, genome editing-based lineage tracing has opened the door to dense cell lineage reconstruction in such organisms3-8, but delivering on that promise has proven technically challenging. Here we apply DNA Typewriter9-11, a prime editing-based recorder that writes stochastic symbolic insertions to an engineered genomic TAPE in strictly sequential order, to trace mouse development from zygote (E0) to late organogenesis (E13.5). We introduce constructs encoding a prime editor, engineered prime editing guide RNAs (epegRNAs), and Pol-III-driven circularized TAPE RNA (circTAPE) into wildtype zygotes by pronuclear injection (PNI), then assay embryos by single-nucleus transcriptional profiling (sci-RNA-seq3)12 with paired circTAPE recovery. From one E13.5 embryo bearing [~]7 integrations of a constitutively expressed prime editor and 11 integrations of 6-unit circTAPE, we recover [~]1.75M single-nucleus transcriptomes and reconstruct a time-calibrated phylogeny of 1,340,794 annotated cells with parsimony-based node support. The first cell division is marked unequivocally, and although the resulting blastomeres contribute asymmetrically to the embryo proper, they are fate-neutral and serve as internal replicates that reproduce every finding. A modest cohort of pre-gastrulation founders dominates the embryo, with inequality exceeding neutral expectation within one to two cell cycles of founder allocation, yet these founders remain broadly multipotent; a second phase of clonal dominance arises in specific lineages during organogenesis. At the finest scale, sibling cells share cell type 9-fold in excess of chance, reaching 68- to 107-fold for cell types arising from spatially restricted founder pools, while the recent differentiations of organogenesis are legible in the heterotypic structure of terminal clades. From clade co-occurrence alone, we recover germ-layer organization and a dated hierarchy of cell-type couplings with branch points from E8.5 onwards. Finally, by integrating these data with our single-cell time-series of mouse development13, we impute transcriptional states and annotations for the majority of internal nodes and recover established state histories for diverse cell types. All data are made freely available, together with NextCell, an interactive browser for this annotated cellular phylogeny of mouse development from zygote to late organogenesis.

developmental biology↗

bioRxiv2026

Non-invasive in vivo lactate monitoring via NIR spectroscopy

We present a non-invasive approach for continuous monitoring of lactate dynamics in-vivo using near-infrared (NIR) spectroscopy. Lactate-related spectral features were measured non-invasively within the overtone region (1600-1850 nm). Several anatomical measurement sites were evaluated, and the middle phalanx of the dorsal finger emerged as the most promising location due to its superior spectral quality and stable tissue perfusion, becoming the exclusive site for all further experiments. Across multiple exercise sessions, predictive models achieved high within-day accuracy (R2[≥] 0.8), while cross-day performance was affected by spectral drift and physiological variability. A dynamic offset-correction procedure effectively mitigated these baseline shifts, enabling stable prediction accuracy across days, weeks, and subjects. These findings demonstrate the feasibility of NIR-based lactate estimation and highlight the importance of adaptive correction strategies for reliable long-term, non-invasive monitoring.

biophysics↗

bioRxiv2025

A Computer Vision Dataset for Pollinator Detection under Real Field Conditions

The widespread decline in biodiversity and abundance of pollinator insects is expected to provoke cascading effects on food security and jeopardize ecosystem services crucial for many crops and wild plants. Pollinator monitoring is a crucial element in preventing further decline of pollinators, to which computer vision approaches can make essential contributions. To facilitate research in such approaches, we present a dataset for pollinator detection with accurate annotations. We develop the dataset with an iterative semi-automatic annotation approach, which leverages YOLO to assist with human annotation. We quantify the impact of multiple levels of errors in annotations on training and report the increase in mAP of 28.7% at the final iteration when compared to the manual annotations. Our dataset encompasses pollinator detection for honeybees and bumblebees across various flower treatments over multiple days. Our dataset facilitates the development of deep learning-based methods for automatic large-scale pollinator detection under various real-world field conditions, as well as adjacent computer vision tasks such as small object detection and label correction.

ecology↗

bioRxiv2025

Cell lineage inference from mitochondrial mutations

Tracing genealogical relationships between cells and their molecular states is essential for understanding tissue development, maintenance and disease. We present mt-SCITE, a computational method that reconstructs cell division histories from mitochondrial mutations in single cells. On simulated and experimental benchmarks, mt-SCITE outperformed existing methods in terms of cell lineage reconstruction, and scaled to thousands of cells. Applied to expanded CD8+ T cells following vaccination, mt-SCITE resolved subclonal relationships and revealed clones with predominantly memory-like, effector-like, or mixed states. mt-SCITE robustly identifies clonally related populations and resolves substructure within them, providing a broadly applicable approach to study cell lineage development.

bioinformatics↗

bioRxiv2025

Assessing the inference of single-cell phylogenies and population dynamics from genetic lineage tracing data

Multicellular organisms develop from a single cell by repeated rounds of cell division, differentiation, and death, which can be represented as a single-cell phylogenetic tree. Genetic lineage tracing allows us to investigate this development by tracking the ancestry of individual cells as populations grow and change over time. However, accurate reconstruction of the cell phylogeny and quantification of the corresponding phylodynamic parameters - cell division, differentiation and death rates - from this tracking data remains challenging and needs to be systematically evaluated. We perform simulations and assess, using the Bayesian framework, the joint inference of time-scaled cell phylogenies and phylodynamic parameters from CRISPR lineage recordings with random or sequential edits. Principally, we characterize the inference improvements as the recorder capacity increases. We observe more accurate phylogenetic reconstruction from sequential compared to random recordings, but no substantial improvement in phylodynamic inference when using the additional information contained in the order of edits. Overall, we find that CRISPR lineage recordings carry a strong signal on the rates of cell division when appropriate models are used. However, we detect biases in the inferred rates of cell division and death under phylodynamic model misspecification, i.e. when fitting classic memoryless birth-death processes to synchronous cell divisions. Moreover, for scenarios when cells differentiate into distinct types, we demonstrate that Bayesian phylodynamic analysis of sparse end-point measurements can resolve these cell differentiation trajectories by lineage and time. Under prototypical dynamics, we recover cell type-specific division and death rates, and cell type transition rates in over 80% of simulations. Overall, this simulation study explores how much information on cellular development can be extracted from state-of-the-art genetic lineage tracing data using phylogenetic and phylodynamic methodology. Author summaryNovel technologies provide means to trace the development of cell populations over time by introducing heritable and editable genetic sequences that record lineage information in the cells genome. Reconstructing a populations history from such sequences sparsely sampled at a single time point is, however, computationally challenging. In this work, we use simulations and statistical inference to evaluate how accurately we can recover the relationships among cells and estimate the temporal dynamics of cell populations from genetic lineage tracing data generated from distinct recording systems, and compare their information content. Our results show that it is possible to quantify how cells divide, differentiate, and die based on such data, though certain statistical limitations remain. Addressing these limitations in future research will be essential for deepening our understanding of cell development in complex tissues and organisms, in both health and disease.

cell 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↗

bioRxiv2023

Uncertainty in crop phenology simulations is driven primarily by parameter variability

It has become common to compare crop model results in multi-model simulation experiments. In general, one observes a large variability in such studies, which reduces the confidence one can have in such models. It is important to understand the causes of this variability as a first step toward reducing it. For a given data set, the variability in a multi-model study can arise from uncertainty in structure or in parameter values for a given structure. Previous studies have made assumptions about the origin of parameter uncertainty, and then quantified its contribution, generally finding that parameter uncertainty is less important than structure uncertainty. However, those studies do not take account of the full parameter variability in multi-model studies. Here we propose estimating parameter uncertainty based on open-call multi-model ensembles where the same structure is used by more than one modeling group. The variability in such a case is due to the full variability of parameters among modeling groups. Then structure and parameter contributions can be estimated sing random effects analysis of variance. Based on three multi-model studies for simulating wheat phenology, it is found that the contribution of parameter uncertainty to total uncertainty is, on average, more than twice as large as the uncertainty from structure. A second estimate, based on a comparison of two different calibration approaches for multiple models leads to a very similar result. We conclude that improvement of crop models requires as much attention to parameters as to model structure.

plant biology↗

bioRxiv2022

Calibration of crop phenology models: Going beyond recommendations

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.

plant biology↗

bioRxiv2022

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

The development of a multicellular organism is governed by an elaborate balance between cell division, death, and differentiation. These core developmental processes can be quantified from single-cell phylogenies. Here we present TiDeTree, a Bayesian phylogenetic framework for inference of time-scaled single-cell phylogenies and population dynamic parameters such as cell division, death, and differentiation rates from genetic lineage tracing data. We show that the performance of TiDeTree can be improved by incorporating multiple sources of additional independent information into the inference. Finally, we apply TiDeTree to a lineage tracing dataset to estimate time-scaled phylogenies, cell division, and apoptosis rates. We envision TiDeTree to find wide application in single-cell lineage tracing data analysis which will improve our understanding of cellular processes during development. The source code of TiDeTree is publicly available at https://github.com/seidels/tidetree.

developmental biology↗

bioRxiv2020

Estimating disease spread using structured coalescent andbirth-death models: A quantitative comparison

Understanding how disease transmission occurs between subpopulations is critically important for guiding disease control efforts irrespective of whether the subpopulations represent geographically separated people, age or risk groups. The structured coalescent (SC) and the multitype birth-death (MBD) model can both be used to infer migration rates between subpopulations from phylogenies reconstructed from pathogen genetic sequences. However, the two classes of phylodynamic methods rely on different assumptions. Here, we report on a simulation study which compares inferences made using these models for a variety of migration rates in both endemic diseases and epidemic outbreaks. For the epidemic outbreak, we found that the MBD recovers the true migration rates better than the SC regardless of migration rate. We hypothesize that the inaccurate SC estimates stem from the its assumption of a constant population size. For the endemic scenario, our analysis shows that both models obtain a similar coverage of the migration rates, while the SC provides slightly narrower posterior intervals. Irrespective of the scenario, both models estimate the root location with similar coverage. Our study provides concrete modelling advice for infectious disease analysts. For endemic disease either model can be used, while for epidemic outbreaks the MBD should be the model of choice. Additionally, our study reveals the need to develop the SC further such that varying population sizes can easily be taken into account. Author summaryControlling an infectious disease requires us to quantify and understand how it spreads through pools of susceptible individuals, defined by their belonging to different geographical regions, age or risk groups. Rates of pathogen movement between these pools can be inferred from pathogen phylogenies which are themselves reconstructed from pathogen genetic sequences collected from infected individuals. Two popular foundations for such models are the multitype birth-death model and the structured coalescent. Although these models fulfill the same purpose, they differ in their assumptions and can, hence, produce contrasting results. To assess the appropriateness of the models in different situations, we performed a simulation study. We find that, for endemic diseases, both models are able to estimate the migration parameters reliably. For epidemic outbreaks, however, the multitype birth-death model obtains better estimates of the migration rates. We hypothesize that the structured coalescents inaccurate estimates for the epidemic scenario arise because it assumes a constant number of infected individuals through time.

bioinformatics↗

bioRxiv2020

Within-patient genetic diversity of SARS-CoV-2

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

genomics↗