Biology subjects

Forsgren, E.

Publications and source records attributed to Forsgren, E..

7 recordsLinked to original sources

bioRxiv2026

Female-biased sex ratios despite stable genetic sex determination across a climatic gradient in a marine fish

Adult sex ratio (ASR) is a fundamental parameter shaping population dynamics and evolutionary trajectories. In theory, ASR is governed by the sex determination (SD) system and sex-specific mortality. Yet, ASR is often hard to predict in natural populations because of ecological effects on its determinants. We address this ecological complexity by investigating the drivers of ASR across a steep climatic gradient in Scandinavian populations of the two-spotted goby Pomatoschistus flavescens. Demographic surveys accounting for over 25000 fish in 30 locations, spanning 10{degrees} of latitude, revealed a general female-bias of the ASR, ca. 75%. To assess the contribution of SD, we combined reduced representation sequencing of 180 adults with our novel long-read male genome assembly. We characterised the genetic architecture of an XX/XY SD system, and identified a male-specific duplicate from the TGF-{beta} signaling pathway, amhr2y, as a candidate sex-determining gene. Subsequent genotyping of eggs and juveniles showed consistently unbiased sex ratios across latitudes, implying that the female-biased arises at later life stages. Interpreted in the light of historical data, our findings allow us to reject environmental sex determination and sex-biased mortality as important drivers of ASR bias in this species. We propose instead that the female bias originates from sex-specific patterns of habitat distribution, which could result from ecological conditions interacting with the distinct reproductive strategies of females and males. Our results underscore the need to go beyond primary theoretical expectations to understand fundamental demographic parameters within their relevant ecological context.

evolutionary biology↗

bioRxiv2025

Label-Free Live-Cell Imaging improves Mode of Action Classification

Morphological profiling is a common approach to investigate the modes of action (MOAs) of compounds. Most methods rely on fixed-cell assays, which provide only a single snapshot at a predefined time point and overlook the dynamic nature of cellular responses. In contrast, live-cell imaging tracks responses over time, offering deeper insight into compound-specific effects and mechanisms; however, time-series analysis of image data remains challenging due to limited analytical tools. We present Live Cell Temporal Profiling (LCTP), a workflow for morphological profiling of label-free live-cell time series data that yields interpretable, biologically relevant results. We showcase LCTP in an MOA classification study using label-free data. The workflow integrates established deep-learning components, cell segmentation, live/dead classification, and single-cell feature extraction, with data-driven models to capture MOA-specific temporal phenotypes and produce time-resolved profiles that can be compared across compounds and cell lines. We assess MOA classification performance using double-blinded cross-validation simulating a real-world screening scenario. LCTP significantly improves MOA classification over single-time point analysis, consistently across both cell lines used in the study. Time-resolved phenotypic modeling reveals transient, sustained, and delayed responses, clarifying compound-specific temporal effects and mechanisms across MOAs. The presented workflow is modular: each step removes irrelevant information, enriching signal, and enabling straightforward updates as technologies evolve and as new technologies become available, while supporting reuse across studies broadly. We believe LCTP adds substantial value to high-throughput compound screening, showing that live-cell imaging combined with this workflow yields informative visualizations of temporal effects and improved MOA classification.

bioinformatics↗

bioRxiv2024

OPLS-based Multiclass Classification and Data-Driven Inter-Class Relationship Discovery

Multiclass datasets and large-scale studies are increasingly common in omics sci-ences, drug discovery, and clinical research due to advancements in analytical platforms. Efficiently handling these datasets and discerning subtle differences across multiple classes remains a significant challenge. In metabolomics, two-class OPLS-DA (Orthogonal Projection to Latent Structures Discriminant Analysis) models are widely used due to their strong discrimination capa-bilities and ability to provide interpretable information on class differences. However, these models face challenges in multiclass settings. A common solution is to transform the multiclass comparison into multiple two-class comparisons, which, while more ef-fective than a global multiclass OPLS-DA model, unfortunately results in a manual, time-consuming model-building process with complicated interpretation. Here, we introduce an extension of OPLS-DA for data-driven multiclass classifi-cation: Orthogonal Partial Least Squares-Hierarchical Discriminant Analysis (OPLS-HDA). OPLS-HDA integrates Hierarchical Cluster Analysis (HCA) with the OPLS-DA framework to create a decision tree, addressing multiclass classification challenges and providing intuitive visualization of inter-class relationships. To avoid overfitting and ensure reliable predictions, we use cross-validation during model building. Benchmark results show that OPLS-HDA performs competitively across diverse datasets compared to eight established methods. This method represents a significant advancement, offering a powerful tool to dissect complex multiclass datasets. With its versatility, interpretability, and ease of use, OPLS-HDA is an efficient approach to multiclass data analysis applicable across various fields.

bioinformatics↗

bioRxiv2023

A scalable, data analytics workflow for image-based morphological profiles

Cell Painting is an established community-based, microscopy-assay platform that provides high-throughput, high-content data for biological readouts. In November 2022, the JUMP-Cell Painting Consortium released the largest annotated, publicly available dataset, comprising more than 2 billion cell images. This dataset is designed for predicting the activity and toxicity of 100k drug compounds, with the aim to make cell images as computable as genomes and transcriptomes. In this paper, we have developed a data analytics workflow that is both scalable and computationally efficient, while providing significant, biologically relevant insights for biologists estimating and comparing the effects of different drug treatments. The two main objectives proposed include: 1) a simple, yet sophisticated, scalable data analytics metric that utilizes negative controls for comparing morphological cell profiles. We call this metric the equivalence score (Eq. score). 2) A workflow to identify and amplify subtle morphological image profile changes caused by drug treatments, compared to the negative controls. In summary, we provide a data analytics workflow to assist biologists in interpreting high-dimensional image features, not necessarily limited to morphological ones. This enhances the efficiency of drug candidate screening, thereby streamlining the drug development process. By increasing our understanding of using complex image-based data, we can decrease the cost and time to develop new, life-saving treatments. Author summaryMicroscopy-assays are often used to study cell responses to treatments in the search for new drugs. In this paper, we present a method that simplifies the understanding of the data generated from such assays. The data in this study consists of 750 morphological features, which describe the traits and characteristics of the cells, extracted from the images. By using untreated cells as a biological baseline, were able to detect subtle changes that occur in the treated cells. These changes are then transformed into an equivalence score (Eq. score), a metric that lets us compare the similarities among different treatments relative to our baseline of untreated cells. Our Eq. score approach transforms complex, high-dimensional data about cell morphology into something more interpretable and understandable. It reduces the "noise" in the features and highlights important changes, the "signal". Our method can be integrated into existing workflows, aiding researchers in understanding and interpreting complex morphological data derived from cell images more easily. Understanding cell morphology is crucial to deepening our knowledge of biological systems. Ultimately, this could contribute to the faster and more cost-effective development of new, life-saving treatments.

cell biology↗

bioRxiv2022

High-throughput widefield fluorescence imaging of 3D samples using deep learning for 2D projection image restoration

Fluorescence microscopy has become a core tool for visualizing and quantifying the spatial and temporal dynamics of complex biological processes. Thanks to its low cost and ease-of-use, widefield fluorescent imaging remains one of the most widely used fluorescence microscopy imaging modalities. To accomplish imaging of 3D samples, conventional fluorescence imaging entails acquiring a sequence of 2D images spaced along the z-dimension, typically called a z-stack. Oftentimes, the next step is to project the 3D volume into a single 2D image, as 3D image data can be cumbersome to manage and challenging to analyze and interpret, effectively limiting the utlity of z-dimensional information. Furthermore, z-stack acquisition is often time-consuming and consequently may induce photodamage to the biological sample, which are both major hurdles for its application in experiments that require high-throughput, such as drug screening. As an alternative to z-stacks, axial sweep acquisition schemes have been proposed to circumvent these drawbacks and offers potential of 100-fold faster image acquisition for 3D-samples compared to z-stack acquisition but unfortunately results in blurry, low-quality raw 2D z-projected images. We propose a novel workflow to combine axial z-sweep acquisition with deep learning-based image restoration, ultimately enabling high-throughput and high-quality imaging of complex 3D-samples using 2D projection images. To demonstrate the capabilities of our proposed workflow, we apply it to live-cell imaging of 3D tumor spheroids and find we can produce high-fidelity images appropriate for quantitative analysis. Therefore, we conclude that combining axial z-sweep image acquisition with deep learning-based image restoration enables high-throughput and high-quality fluorescence imaging of complex 3D biological samples.

cell biology↗

bioRxiv2021

Efficacy of two commercial disinfectants on Paenibacillus larvae spores

SHORT SUMMARYPaenibacillus larvae is a spore-forming bacterium causing American foulbrood (AFB) in honey bee larvae. The remains of a diseased larva contains billions of extremely resilient P. larvae spores viable for decades. Burning clinically symptomatic colonies is widely considered the only workable strategy to prevent further spread of the disease, and the management practices used for decontamination requires high concentrations of chemicals or special equipment. The aim of this study was to test and compare the biocidal effect of two commercially available disinfectants, " Disinfection for beekeeping" and Virkon S on P. larvae. The two products were applied to P. larvae spores in suspension as well as inoculated on two common beehive materials, wood and styrofoam. " Disinfection for beekeeping" had a 100 % biocidal effect on P. larvae spores in suspension compared to 87.0-88.6 % for Virkon S which, however, had a significantly better effect on P. larvae on styrofoam. The two disinfectants had similar effect on infected wood material.

microbiology↗

bioRxiv2018

The molecular pathogenesis of superoxide dismutase 1-linked ALS is promoted by low oxygen tension

Mutations that destabilize superoxide dismutase 1 (SOD1) are a cause of amyotrophic lateral sclerosis (ALS). SOD1, which is located in the reducing cytosol, contains an oxidized disulfide bond required for stability. We show that the bond is an Achilles heel of the protein because it is sensitive to the oxygen tension. Culture of ALS patient-derived fibroblasts, astrocytes and induced pluripotent stem cell-derived mixed motor neuron and astrocyte cultures (MNACs) under lowered oxygen tensions caused reductive bond cleavage and misfolding. The effects were greatest in cells expressing mutant SOD1s, but also occurred in wild type SOD1 in cultures derived from patients carrying ALS-linked mutations in C9orf72, FUS and TBK1, as well as from controls. MNACs showed a greater response than the other cell types, including enhanced SOD1 aggregation, in line with the vulnerability of the motor system. Our results show that oxygen tension is a principal determinant of SOD1 stability and shed light on how risk factors for ALS, such as aging and other conditions causing reduced vascular perfusion, could lead to disease initiation and progression.\n\nSubject categories Neuroscience; Molecular Biology of Disease

neuroscience↗