Biology subjects

Lagergren, J.

Publications and source records attributed to Lagergren, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Scalable and robust phylogenetic tree reconstruction from copy-number data with Sparse Rooted Neighbor Joining

BackgroundPhylogenetic tree reconstruction from single cell data based on copy-number alterations (CNAs) is an important problem in cancer genomics. Methods have been developed to address this problem by computing pairwise distances between copy-number profiles and employing a tree reconstruction algorithm. Despite the tight interplay between distance estimation and tree reconstruction, these two steps are often treated as separate problems, with the choice of the reconstruction algorithm receiving little attention. Most methods rely on classical Neighbor Joining (NJ), an algorithm designed for unrooted phylogenies that does not account for the fixed diploid root inherent to copy-number evolution. ResultsWe identify the Deepest Least Common Ancestor NJ (DLCA-NJ), not previously applied in this context, as the appropriate algorithm for phylogenies from copy-number data. By leveraging the known diploid root, it consistently outperforms standard NJ on simulated benchmarks across all evaluated metrics, with the most pronounced improvement in root placement accuracy. Building on these findings, we introduce Sparse Rooted Neighbor Joining (SRNJ), a scalable adaptation of DLCA-NJ. SRNJ significantly reduces running time while trading off only a minor loss in accuracy. We provide theoretical and empirical evidence of robustness to mutation rate using both synthetic and real biological datasets. ConclusionsRooted NJ variants offer a principled way to exploit the known diploid root when reconstructing phylogenies from copy-number data, and SRNJ extends this advantage to datasets whose size places the full distance matrix out of reach. The gains are clearest where distances are reliable, as on simulated data, while on real data accuracy appears to be constrained by distance estimation rather than by the reconstruction algorithm, leaving room for improvement as callers advance.

cancer biology↗

bioRxiv2026

Looking beyond stereotyped neuron structures reveals links between beading and morphological rearrangements in aging phenotypes.

1Understanding how neuronal morphology changes during aging and acute stress is essential for elucidating mechanisms of neurodegeneration. The highly branched PVD neuron of Caenorhabditis elegans provides a powerful model for studying dendritic remodeling and degeneration-associated phenotypes such as dendritic beading. However, the complexity of this arbor presents substantial challenges for automated segmentation and quantitative analysis. In this study, we adapted a convolutional neural network (CNN)-guided region growing framework for automated dendrite tracing, coupled with two topology-based algorithms for categorizing dendritic segments by branching degree. The segmentation algorithm achieved high accuracy relative to manual tracing, with a median Dice coefficient of 0.82, while reducing analysis time by approximately tenfold. Automated dendrite categorization demonstrated strong agreement with manual annotations across branching orders, though position-based mapping performance declined with age due to progressive morphological distortion. Leveraging this platform, we investigated mechanistic differences in dendritic beading patterns observed during aging and cold shock. Consistent with prior work, aging was associated with decreased inter-bead spacing, whereas cold shock produced increased bead dispersion with stress severity. Structural analysis revealed that these trends were not driven by dendritic pruning or reduced arbor complexity. Instead, while a traditional anatomically unflexible paradigm falsely implicated lower-degree dendrites as highly vulnerable, our branching-informed framework revealed that age-dependent beading is fundamentally dictated by a segments history of successive branching events. Conversely, acute cold shock triggered systemic beading that expanded across all dendritic orders in a severity-dependent manner. Together, these findings demonstrate that chronic aging and acute stress engage distinct degenerative pathways (compartment-specific lineage vulnerability versus global architectural collapse) rather than gross morphological loss, as well as highlighting the need for paradigms that enable reliable analysis of changing morphologies.

bioinformatics↗

bioRxiv2025

Populus trichocarpa associated microbiomes vary across a natural temperature, elevation and rainfall gradient

Abiotic stresses associated with warming and drought are increasing in frequency and severity due to climate change. However, microbial associations can partially mitigate their adverse effects on plant physiology. We examined the bacterial/archaeal and fungal communities with 16S rRNA and ITS2 gene region amplicon sequencing of bulk soil, rhizosphere soil, and root endosphere samples of Populus trichocarpa trees from 11 sites along a 200 km transect with highly variable precipitation, temperature, and soil conditions over the Cascade Mountains in Washington, USA. Foliar stress response was examined with qRT-PCR and field physiological measurements. Soil conditions were examined with physiochemical sample analysis and site climate conditions were approximated using DAYMET. For each niche sampled, bacterial/archaeal and fungal communities clustered into three distinct micro-climate groups: a high-precipitation, low-stress community on the western side of the range, and two stressed climate communities: a high-elevation community nearer the crest of the pass, and a warmer and drier community along the eastern slope. The differences between these micro-climate communities, though still distinct, were notably diminished within the P. trichocarpa root endosphere, indicating that P. trichocarpa likely has a homogenizing effect on the microbiome under stress, but climate plays a major role in shaping these microbial communities.

ecology↗

bioRxiv2025

LN's t-Test: A Principled Approach to t-Testing in Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, yet differential gene expression (DGE) analysis remains hindered by inconsistencies in log fold change (LFC) estimation. Existing methods, such as those implemented in Scanpy and Seurat, rely on log-transformed count data with a pseudocount, introducing a bias that compromises the reliability of the statistical inference. In this work, we propose LNs t-test, a novel approach to DGE testing that circumvents these biases by employing a log-Normal (LN) distribution-based LFC estimator. Our method jointly estimates the probability of non-zero expression and the mean of positive expression values, enabling an asymptotically unbiased and normally distributed LFC estimator with corresponding confidence intervals. Through extensive simulation studies, we demonstrate that LNs t-test outperforms competing methods by reducing false discovery rates and providing more accurate effect size estimates. Notably, we leverage stochastic ordering theory to explain why conventional t-tests systematically mis-classify non-differentially expressed genes under realistic variance conditions. Our approach offers a theoretically principled and computationally efficient alternative for DGE analysis in scRNA-seq, with implications for improving the reliability and interpretability of single-cell transcriptomics studies. Code that implements the results is available on GitHub: https://github.com/okviman/DE-ZILN.

bioinformatics↗

bioRxiv2024

Natural variation in physical responses to waterlogging across climate-diverse pennycress accessions

Fluctuations in flooding and differences in hydrological gradients affect the geographic distribution of plant species across ecosystems, resulting in the presence of adaptive traits in populations that confer enhanced fitness in flooded environments. Many winter annual crops, such as pennycress (Thlaspi arvense L.), are subjected to heavy precipitation events during their growing season, which are increasing in frequency due to climate change. Therefore, it is essential to identify pennycress accessions with natural variation in flooding resilience. In this study, we used climate modeling data to assess spring soil moisture levels in the geographic origins of 471 natural pennycress accessions. We selected 34 accessions with variations in predicted soil moisture to test survivability under prolonged waterlogging at the rosette stage. This identified variation in waterlogging tolerance, where six accessions had 0% survivability and nine accessions had 100% survivability. It took at least seven weeks for the first accessions to die under waterlogging, indicating that pennycress is hardy to prolonged waterlogging at the vegetative stage. Furthermore, we chose three "susceptible" and five "tolerant" accessions to waterlog for one week at the reproductive stage, the growth stage aligned with heavy spring rainfall. Six accessions had significantly reduced seed weight at maturity after recovery from waterlogging, and two accessions had minimal impacts on growth and seed yield after waterlogging. These two accessions can be used in future studies to explore adaptive traits, such as changes in root characteristics, as well as the genetic variation that contributes to pennycress waterlogging tolerance.

plant biology↗

bioRxiv2024

Climate adaptation in P. trichocarpa: key adaptive loci identified for stomata and leaf traits

Identifying the genetic basis of traits underlying climate adaptation remains a key goal for predicting species responses to climate change, enabling the elucidation of gene targets for future climate-resilient crops. Here, we measured 14 leaf and stomatal traits under control (well-watered) and drought conditions, subsampling a diversity collection of over 1,300 Populus trichocarpa genotypes, a potential biofuel feedstock crop. Stomatal traits were correlated with the climate of origin for genotypes, such that those originating from environments subject to water deficit tended to have smaller stomata, but with higher density. Stomatal traits were also correlated with leaf morphology, with larger leaves having larger stomata and lower stomatal density mirrored in correlations to climate of origin. The direction of plastic responses - reduced stomatal size under drought - mirrors the correlations seen among genotypes with respect to the aridity of environmental origin. Genome-Wide Association Studies (GWAS) identified loci underlying trait diversity, including candidates contributing to stomatal size. We used climate of origin to predict stomatal size in genotypes with unknown trait values and found that these predicted phenotypes confirmed empirically measured allele effects. Finally, we found evidence that future climates may select for alleles contributing to decreased stomatal size, with the strength of selection depending on the availability of moisture. These findings reveal adaptive variation in stomatal and physiological traits along with underlying genetic loci, with implications for future selection and breeding - providing insights into the responses to future climate change. HighlightResearch on Populus trichocarpa reveals adaptation of physiological and stomatal traits linked to drought tolerance, with genotypes from arid regions exhibiting smaller stomata, offering insights for climate change adaptation and sustainable biofuel production.

plant biology↗

bioRxiv2024

Divide and conquer: Using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. The collection of root samples from the field and their subsequent cleaning and scanning in a water-filled tray ranging in size from 5 to 20 cm, followed by digital image analysis has been commonly used since the 1990s for measuring root length, volume, area, and diameter. However, one common issue has been neglected. Sometimes, the amount of roots for a sample is too much to fit into a single scanned image, so the sample is divided among several scans. There is no standard method to aggregate the root measurements across the scans of the same sample. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. Both methods rely on standardizing file naming conventions to identify scans that belong to the same sample. Image concatenation refers to combining digital images into a single larger image while maintaining the original resolution. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image for every set of images in a directory. These concatenated images (combining up to 10 scans) and the original images were processed with RhizoVision Explorer, a free and open-source software developed for estimating root traits from images, with the same settings. An R script was developed that can identify the rows of data belonging to the same sample in RhizoVision Explorer data files and apply correct statistical methods such as summation, weighted average by length, and average to the appropriate measurement types to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Overall, the new methods accomplished the goal of standardizing measurement aggregation. Most root measurements were nearly identical except median diameter, which can not be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.

plant biology↗

bioRxiv2024

VICTree - a Variational Inference method for Clonal Tree reconstruction

Clonal tree inference brings crucial insights to the analysis of tumor heterogeneity and cancer evolution. Recent progress in single cell sequencing has prompted a demand for more advanced probabilistic models of copy number evolution, coupled with inference methods which can account for the noisy nature of the data along with dependencies between adjacent sites in copy number profiles. We present VICTree, a variational inference based algorithm for joint Bayesian inference of clonal trees, together with a novel Tree-structured Mixture Hidden Markov Model (TSMHMM) which combines HMMs related through a tree with a mixture model. For the tree inference, we introduce a new algorithm, LARS, for sampling directed labeled multifurcating trees. To evaluate our proposed method, we conduct experiments on simulated data and on samples of multiple myeloma and breast cancer. We demonstrate VICTrees capacity for reliable clustering, clonal tree reconstruction, copy number evolution and the utility of the ELBO for model selection. Lastly, VICTrees results are compared in terms of quality and speed of inference to other state-of-the-art methods. The code for VICTree is available on GitHub: github.com/Lagergren-Lab/victree.

cancer biology↗

bioRxiv2023

Environmental and ecological signals predict periods of nutritional stress for Eastern Australian flying fox populations.

Food availability determines where animals use space across a landscape and therefore affects the risk of encounters that lead to zoonotic spillover. This relationship is evident in Australian flying foxes (Pteropus spp; fruit bats), where acute food shortages precede clusters of Hendra virus spillovers. Using explainable artificial intelligence, we predicted months of food shortages from climatological and ecological covariates (1996-2022) in eastern Australia. Overall accuracy in predicting months of low food availability on a test set from 2018 up to 2022 reached 93.33% and 92.59% based on climatological and bat-level features, respectively. Seasonality and Oceanic El Nino Index were the most important environmental features, while the number of bats in rescue centers and their body weights were the most important bat-level features. These models support predictive signals up to nine months in advance, facilitating action to mitigate spillover risk.

ecology↗

bioRxiv2022

C-ToMExO: Learning Cancer Progression Dynamics from Clonal Composition of Tumors

Cancer is an evolutionary process involving the accumulation of somatic mutations in the genome. The tumors evolution is known to be highly influenced by specific somatic mutations in so-called cancer driver genes. Cancer progression models are computational tools used to infer the interactions among cancer driver genes by analyzing the pattern of absence/presence of mutations in different tumors of a cohort. In an abundance of subclonal mutations, discarding the heterogeneity of tumors and investigating the interrelations among the driver genes solely based on tumor-level data can result in misleading interpretations. In this paper, we introduce a computational approach to infer cancer progression models from the clone-level data gathered from a cohort of tumors. Our method leverages the rich clone-level data to identify the patterns of interactions among cancer driver genes and produce significantly more robust and reliable cancer progression models. Using a novel efficient Markov Chain Monte Carlo inference algorithm, our method provides outstanding scalability to the rapidly increasing size of available datasets. Using an extensive set of synthetic data experiments, we demonstrate the performance of our inference method in recovering the generative progression models. Finally, we present our analysis of two sub-types of lung cancer using biological multi-regional bulk data.

bioinformatics↗

bioRxiv2022

Spatial transcriptomics of T and B cell receptors uncovers lymphocyte clonal dynamics in human tissue.

The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed Spatial Transcriptomics of VDJ sequences (Spatial VDJ), which maps immunoglobulin and TR antigen receptors in human tissue sections. Spatial VDJ captures lymphocyte clones matching canonical T, B, and plasma cell distributions in tissues and amplifies clonal sequences confirmed by orthogonal methods. We confirm spatial congruency between paired receptor chains, develop a computational framework to predict receptor pairs, and link the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity, class switch recombination, and lineage trajectories within their spatial context. Taken together, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, which could have important therapeutic implications. One-Sentence SummarySpatial transcriptomics-based technology co-captures T and B cell receptors within their anatomical niche in human tissue.

immunology↗

bioRxiv2022

Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues

Spatial and genomic heterogeneity of tumors is the key for cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their high-resolution localization by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

cancer biology↗

bioRxiv2022

Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperformed other methods on simulated data, successfully indicated known brain structures and spatially distinguished between inhibitory and excitatory neuron types based in mouse brain tissue, and dissected large heterogeneity of immune infiltrate composition in prostate gland tissue.

bioinformatics↗

bioRxiv2022

Clonally heritable gene expression imparts a layer of diversity within cell types

Cell types can be classified based on shared patterns of transcription. Variability in gene expression between individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. We asked whether long-term, heritable differences in transcription can impart diversity within a cell type. Studying clonal human lymphocytes and mouse brain cells, we uncover a vast diversity of heritable transcriptional states among different clones of cells of the same type in vivo. In lymphocytes we show that this diversity is coupled to clone specific chromatin accessibility, resulting in distinct expression of genes by different clones. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging and disease.

systems biology↗

bioRxiv2022

Tree of mutually exclusive oncogenes

Identifying cancer driver genes and their interrelations is critical in understanding cancer progression mechanisms. In this paper, we introduce ToMExO, a probabilistic method to infer cancer driver genes and how they affect each other, using cross-sectional data from cohorts of tumors. We model cancer progression dynamics using a tree with sets of driver genes in the nodes. This model explains the temporal orders among driver mutations and their mutual exclusivity patterns. We introduce a dynamic programming procedure for the likelihood calculation and build an MCMC inference algorithm. Together with our engineered MCMC moves, our efficient likelihood calculations enable us to work with datasets having hundreds of genes and thousands of tumors in the datasets. We demonstrate our methods performance on several synthetic datasets covering various scenarios for cancer progression dynamics. We then present the analyses of several biological datasets using the ToMExO method and validate the results using a set of method-independent metrics.

cancer biology↗

bioRxiv2021

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data

MotivationDNA Methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods employ either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. ResultsHere we present DeepMP, a convolutional neural network (CNN)-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on E. coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. AvailabilityDeepMP is implemented and freely available under MIT license at github.com/pepebonet/DeepMP Contactjose.bonet@irbbarcelona.org -- mandiche@kth.se

bioinformatics↗

bioRxiv2021

PhylEx: Accurate reconstruction of clonal structure via integrated analysis of bulk DNA-seq and single cell RNA-seq data

We propose PhylEx: a clonal-tree reconstruction method that integrates bulk genomics and single-cell transcriptomics data. In addition to the clonal-tree, PhylEx also assigns single-cells to clones, which effectively produce clonal expression profiles, and generates clonal genotypes. By analyzing scRNA-seq integrated with bulk DNA-seq, PhylEx can take advantage of co-occurrences of the mutations found in the cells. In the probabilistic model underlying PhylEx, the raw read counts from scRNA-seq follow a mixture of Beta-Binomial distributions, which accounts for the sparse nature of single-cell gene expression data; the mixture lessens the penalty caused by mutations not observed due to mono-allelic expression. We rigorously evaluated PhylEx on simulated datasets as well as a biological dataset consisting of a previously well-characterized high-grade serous ovarian cancer (HGSOC) cell line. PhylEx outperformed the state-of-the-art methods by a wide margin both when comparing capacity for clonal-tree reconstruction and capacity for correctly clustering mutations. By analyzing HGSOC and HER2+ breast cancer data, we also show that PhylEx clears the way for phylo-phenotypic analysis of cancer, i.e., that the clonal expression profiles, induced by the cell-to-clone assignments, can be exploited in a manner beyond what is possible with only expression-based clustering.

bioinformatics↗

bioRxiv2020

Inferring Tumor Progression in Large Datasets

Identification of mutations of the genes that give cancer a selective advantage is an important step towards research and clinical objectives. As such, there has been a growing interest in developing methods for identification of driver genes and their temporal order within a single patient (intra-tumor) as well as across a cohort of patients (inter-tumor). In this paper, we develop a probabilistic model for tumor progression, in which the driver genes are clustered into several ordered driver pathways. We develop an efficient inference algorithm that exhibits favorable scalability to the number of genes and samples compared to a previously introduced ILP-based method. Adopting a probabilistic approach also allows principled approaches to model selection and uncertainty quantification. Using a large set of experiments on synthetic datasets, we demonstrate our superior performance compared to the ILP-based method. We also analyze two biological datasets of colorectal and glioblastoma cancers. We emphasize that while the ILP-based method puts many seemingly passenger genes in the driver pathways, our algorithm keeps focused on truly driver genes and outputs more accurate models for cancer progression. Author summaryCancer is a disease caused by the accumulation of somatic mutations in the genome. This process is mainly driven by mutations in certain genes that give the harboring cells some selective advantage. The rather few driver genes are usually masked amongst an abundance of so-called passenger mutations. Identification of the driver genes and the temporal order in which the mutations occur is of great importance towards research and clinical objectives. In this paper, we introduce a probabilistic model for cancer progression and devise an efficient inference algorithm to train the model. We show that our method scales favorably to large datasets and provides superior performance compared to an ILP-based counterpart on a wide set of synthetic data simulations. Our Bayesian approach also allows for systematic model selection and confidence quantification procedures in contrast to the previous non-probabilistic progression models. We also study two large datasets on colorectal and glioblastoma cancers and validate our inferred model in comparison to the ILP-based method.

cancer biology↗