Biology subjects

Danielsson, M.

Publications and source records attributed to Danielsson, M..

6 recordsLinked to original sources

bioRxiv2026

Inferring differentiation trajectories from T cell clonotype distributions

Single-cell RNA sequencing (scRNA-seq) enables reconstruction of cellular differentiation trajectories, but most trajectory inference methods rely largely on gene expression and overlook lineage relationships. In T lymphocytes, the T cell receptor (TCR) provides a unique, endogenous barcode that is stably inherited during clonal expansion. Here we introduce PhyloTrajectory, a framework that leverages TCR-based clonotype frequencies together with scRNA-seq to infer T cell differentiation dynamics. By modeling clonotype frequency evolution as a continuous stochastic process, PhyloTrajectory reconstructs differentiation tree topologies from clonotype frequencies across cellular subsets. We investigate the ability to recover the underlying tree topology using simulated data, and apply our approach to CD4+ T cells in murine models of allergic inflammation and viral infection. We further demonstrate that PhyloTrajectory is useful in the context of exogenously introduced barcodes. Overall, this shows that a stochastic model of clonal expansion can be used to infer cell state transitions from clonotype frequencies.

bioinformatics↗

bioRxiv2025

difFUBAR: Scalable Bayesian comparison of adaptive evolution

While many phylogenetic methods exist to characterize evolutionary pressure at individual codon sites, relatively few allow direct comparison between different a priori selected sets of branches. Such comparisons may be useful for pinpointing precisely the codon sites that are under differing selective pressures due to differing environmental contexts, or differing genomic contexts via epistatic interactions. Indeed, this was only recently addressed by an approach, developed in the frequentist framework, that proposes a site-wise likelihood ratio hypothesis test. Previously, we have demonstrated that approximate grid-based Bayesian approaches to characterizing site-wise variation in selection parameters can outperform individual site-wise likelihood ratio tests. Such grid-based approaches can exhibit poor computational scaling when the number of site-wise parameters expands, but here we show that this is still tractable up to four parameters, and that a simple subtree-likelihood caching strategy can provide efficiency improvements in some cases. We propose difFUBAR, which allows the demarcation of two branch sets of interest and, optionally, a background set, and estimates joint site-specific posterior distributions over , {omega}1,{omega} 2, and{omega} BG using a Gibbs sampler. Evidence for hypotheses of interest can then be quantified directly from the posterior distribution, and we standardly report P ({omega}1 > {omega}2), P ({omega}2 > {omega}1), P ({omega}1 > 1), and P ({omega}2 > 1). We characterize the computational and statistical performance of this approach on previous simulations, comparing it to the site-wise likelihood ratio test approach, where it shows moderate statistical benefits, and substantial computational gains, typically being more than two orders of magnitude faster on the same datasets. We also demonstrate that it can scale to datasets of over ten thousand taxa, on a laptop in under ten minutes. difFUBAR is implemented in MolecularEvolution.jl - a Julia framework for phylogenetic model development - and can be run locally, or online via a Colab notebook.

bioinformatics↗

bioRxiv2019

Combined mRNA and protein single cell analysis in a dynamic cellular system using SPARC

Combined measurements of mRNA and protein expression in single cells enables in-depth analysis of cellular states. We present single-cell protein and RNA co-profiling (SPARC), an approach to simultaneously measure global mRNA and large sets of intracellular protein in individual cells. Using SPARC, we show that mRNA expression fails to accurately reflect protein abundance at the time of measurement in human embryonic stem cells, although the direction of changes of mRNA and protein expression are in agreement during cellular differentiation. Moreover, protein levels of transcription factors better predict their downstream effects than do the corresponding transcripts. We further show that changes of the balance between protein and mRNA expression levels can be applied to infer expression kinetic trajectories, revealing future states of individual cells. Finally, we highlight that mRNA expression may be more varied among cells than levels of the corresponding proteins. Overall, our results demonstrate that mRNA and protein measurements in single cells provide different and complementary information regarding cell states. Accordingly, SPARC can offer valuable insights in gene expression programs of single cells.

cell biology↗

bioRxiv2019

Loss of Y in leukocytes, dysregulation of autosomal immune genes and disease risks

Mosaic loss of chromosome Y (LOY) in leukocytes has been associated with many diseases, yet it remains unclear whether this form of clonal mosaicism exerts a direct physiological effect. Here we perform single-cell and bulk RNA sequencing in leukocytes, observing considerable variation in the rate of LOY across individuals, cell types and disease state. Cells with LOY demonstrated a profound degree of transcriptional dysregulation impacting [~]500 autosomal genes. These genes are preferentially involved in immune functions but also encode proteins with roles in other diverse biological processes. Our findings highlight a surprisingly broad role for chromosome Y challenging the view of it as a "genetic wasteland". Furthermore, they support the hypothesis that altered immune function in leukocytes is a mechanism directly linking LOY to disease.

immunology↗

bioRxiv2019

Intra-individual changes in the frequency of mosaic loss of chromosome Y over time estimated with a new method

BackgroundMosaic loss of chromosome Y (LOY) is the most common somatic mutation and is associated with all-cause mortality, non-haematological cancers and Alzheimers disease among other outcomes. The predominant method used for estimating LOY is the intensity data generated by SNP-arrays, which is difficult to interpret due to its logarithmic scale. Here we describe a new way to convert the LOY mosaicism into a non-logarithmic scale, which instead represents the percentage of affected cells.\n\nMethodsWe compared three independent LOY readouts from matched samples, generated by SNP-array, whole genome sequencing and droplet digital PCR. The SNP-array standardization was derived from this comparison and was applied in analyses of serially collected samples from a large cohort of aging men. The sampling was performed up to five times, spanning up to 22 years.\n\nResultsWe observed a higher correlation between the LOY measurements from SNP-array and the two other readouts when using the standardized, instead of the logarithmic, SNP-array data. We also observed a pronounced intra-individual variation of changes in the frequency of LOY within individual males over time.\n\nConclusionsDescribing LOY measurements generated from SNP-arrays in percentage of cells without the Y chromosome makes comparisons to WGS and ddPCR measurements more precise and easier to interpret. This standardization could be applied to the vast amount of SNP-array data already generated in the scientific community, allowing further discoveries of LOY associated disease and outcomes. Additionally, the frequency of LOY in this study changed profoundly within men over time, likely as a result of aberrant clonal expansions.

genetics↗

bioRxiv2019

Genetic predisposition to mosaic Y chromosome loss in blood is associated with genomic instability in other tissues and susceptibility to non-haematological cancers

Mosaic loss of chromosome Y (LOY) in circulating white blood cells is the most common form of clonal mosaicism, yet our knowledge of the causes and consequences of this is limited. Using a newly developed approach, we estimate that 20% of the UK Biobank male population (N=205,011) has detectable LOY. We identify 156 autosomal genetic determinants of LOY, which we replicate in 757,114 men of European and Japanese ancestry. These loci highlight genes involved in cell-cycle regulation, cancer susceptibility, somatic drivers of tumour growth and cancer therapy targets. Genetic susceptibility to LOY is associated with non-haematological health outcomes in both men and women, supporting the hypothesis that clonal haematopoiesis is a biomarker of genome instability in other tissues. Single-cell RNA sequencing identifies dysregulated autosomal gene expression in leukocytes with LOY, providing insights into how LOY may confer cellular growth advantage. Collectively, these data highlight the utility of studying clonal mosaicism to uncover fundamental mechanisms underlying cancer and other ageing-related diseases.

genetics↗