Biology subjects

Pilarski, J.

Publications and source records attributed to Pilarski, J..

3 recordsLinked to original sources

bioRxiv2026

scprocess: a pipeline for processing, integrating and visualising atlas-scale single cell data

MotivationThe transition toward "atlas-scale" single cell research has resulted in datasets comprising millions of cells across hundreds of samples, creating significant challenges for data management, computational efficiency, and reproducibility. While numerous methods are available for individual steps in single cell data processing, the highly complex nature of the analysis makes it challenging to maintain a clear record of every tool and parameter used. This makes final results difficult to reproduce, highlighting the need for a unified workflow that integrates multiple steps into a cohesive framework. Resultsscprocess is a Snakemake pipeline designed to streamline and automate the complex steps involved in processing single cell RNA sequencing data. Specifically optimized for data generated using the 10x Genomics technology, it provides a comprehensive solution that transforms raw sequencing files into standardized outputs suitable for a variety of downstream tasks. The pipeline is built to support the analysis of datasets comprising multiple (e.g. 100+) samples via a simple CLI, allowing researchers to efficiently explore their datasets while ensuring reproducibility and scalability in their workflows. Availability and implementationscprocess can be installed via GitHub (https://github.com/marusakod/scprocess) under the MIT license. Documentation, including setup instructions and tutorials on example datasets is available at https://marusakod.github.io/scprocess/.

bioinformatics↗

bioRxiv2025

Bayesian phylodynamics for developmental biology: incorporating age-dependence

As novel technologies for single-cell lineage tracing emerge, phylogenetic and phylodynamic tools are increasingly being used to study developmental processes. However, traditional phylodynamic methods, which were originally developed to study viral evolution, rely on assumptions that are difficult to justify in developmental contexts. Notably, due to cells dividing after characteristic generation times rather than after exponential waiting times--as assumed by the traditionally used birth-death model--empirical cell lineage trees deviate from birth-death phylogenies. Here, we present a non-trivial extension of the birth-death phylodynamic model that captures this characteristic feature of development. By applying our method to a public dataset of stem cell colonies, we show how previous estimates of the underlying population-dynamic parameters were biased by the choice of a birth-death tree prior. Beyond developmental biology, our framework provides an approach for analyzing systems where classical birth-death assumptions may be violated or where empirical tree shapes are poorly captured by those expected under standard phylodynamic models. Our method is available as a BEAST2 package. SignificanceApplying phylodynamic inference methods to data from developmental biology requires reassessment of the foundational assumptions underlying these tools. We show that cell population dynamics can be captured by an age-dependent branching process, as opposed to the widely used birth-death process. We develop computational methodology for efficient phylodynamic inference under this age-dependent model, thus providing a tool for connecting cell population dynamics to lineage trees. Our method is furthermore, to our knowledge, the first performant implementation of an age-dependent phylodynamic likelihood, and may be more generally applicable to systems which are ill-characterized by traditional birth-death models.

developmental biology↗

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↗