Biology subjects

Andersson, A.

Publications and source records attributed to Andersson, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Understanding cognitive function in post-COVID-19 condition with fatigue: an interdisciplinary multimodal investigation

Lingering cognitive symptoms of post-COVID-19 condition (PCC) continue to challenge our understanding, highlighting the need for interdisciplinary research. We investigated whether individuals with PCC and fatigue differ from matched controls across neurocognitive, neuroimaging, and immunological measures, integrating these findings with qualitative phenomenological analyses through multimodal cross-analysis. Individuals with PCC and clinically significant fatigue (n=34), and age-, sex-, and education-matched controls recovered from COVID-19 without PCC (n=37) were included. Assessments encompassed physician evaluation, neuropsychological and functional tests, structural and functional MRI, immunological tests, and qualitative phenomenological analysis of interviews. Data were analyzed using established methods and an interdisciplinary cross-analysis to examine persistent cognitive symptoms in PCC. Group comparisons revealed subtle differences between individuals with PCC and controls, while neurocognitive performance and immunological markers remained within normative ranges. Modest differences in white matter, myelin related, and global brain measures were observed, alongside differences in associations between network connectivity and neurocognitive performance across groups, suggesting reduced network resilience rather than domain specific deficits. This pattern resonated with qualitative phenomenological findings: altered experiences of concentrating and difficulties to execute decisions and actions, despite preserved performance on standard neurocognitive tests. These findings suggest that cognitive symptoms in post-COVID-19 fatigue are associated with subtle neurobiological alterations not captured by standard cognitive assessments. In this highly fatigued sample, converging neuroimaging, neurocognitive and immunological findings point toward reduced network efficiency rather than overt cognitive impairment. Qualitative phenomenological analysis further highlighted altered modes of being in the world characterized by increased cognitive effort and difficulties executing decisions and actions.

neuroscience↗

bioRxiv2026

Spatial Decoding of Tertiary Lymphoid Structure Maturation in Non-Small Cell Lung Cancer Using Deep Neural Networks

Understanding the role of tertiary lymphoid structures (TLS) is crucial in non-small cell lung cancer (NSCLC), as they are associated with patient prognosis and treatment outcomes. Specific cellular ecosystems that originate anti-tumor activity or predict immunotherapy response remain poorly characterized. To this end, we developed a high-resolution, multimodal spatial atlas jointly profiling transcriptomics, proteomics, and histology to characterize TLS maturation in NSCLC alongside secondary lymph organs as a baseline. Using this atlas, we proposed a pathologist-in-the-loop framework that combines a variational graph autoencoder (VGAE) with diffusion pseudotime to refine human expert annotations and characterize TLS maturation. These spatial molecular representations were extended to H&E whole-slide images via a vision transformer-based foundation model. Next, we resolved cellular composition, spatial organization, and cell-cell interactions within these data and defined two divergent spatial ecosystems. Clinical evidence suggests that these ecosystems are associated with distinct patient outcomes: a mature germinal center niche with favorable prognoses, and a tumor-macrophage-fibroblast niche with unfavorable prognoses. In summary, our work decodes key components of TLS heterogeneity, identifies hallmark spatial patterns involved in NSCLC adaptive immunity, and provides a framework for translating spatial omics insights into clinical applications.

genomics↗

bioRxiv2025

Inferring cellular communication through mapping cells in space using Tangram 2

Cell-to-cell communication (CCC) shapes development, immunity, and disease, yet current spatial transcriptomics (SRT) platforms rarely achieve both single-cell resolution and high-quality transcriptome-wide coverage to accurately characterize CCC in the tissue microenvironment. Tangram2 bridges this gap by integrating single-cell RNA sequencing (scRNA-seq) with SRT to identify genes whose expression changes as a function of neighboring cell types. By accurately mapping cells in tissue space, Tangram2 disentangles interaction-driven transcriptional shifts from intrinsic identity markers, yielding mechanistic CCC maps. Validation across diverse settings--including Slide-tags, co-cultured experiments, human lymph nodes, and simulations--demonstrates high accuracy. Applied to triple-negative breast cancer (TNBC) and cutaneous squamous cell carcinoma (cSCC), Tangram2 recapitulates known biology and uncovers new hypotheses, including various immunosuppressive mechanisms in TNBC and a macrophage-regulatory T-cell circuit associated with survival in cSCC.

genomics↗

bioRxiv2025

Low-dimensional brain-symptom associations delineate depression phenotypes with distinct connectivity biomarkers and symptom profiles

Depression is neurobiologically and clinically heterogeneous. New approaches using resting-state functional MRI (rs-fMRI) functional connectivity (FC) data have modeled the neural basis of depression heterogeneity and revealed unique neural phenotypes. Yet, no studies have identified depression phenotypes from electrophysiological magnetoencephalography (MEG) data although MEG measures human brain dynamics at millisecond precision. We demonstrate here unique depression phenotypes based on MEG-oscillation FC. We collected resting-state MEG, MRI, and clinical symptom data from 263 patients with unipolar depression and 75 healthy controls. We assessed MEG-FC with two oscillatory coupling-mode measures that are fundamental for information processing. To define normative phenotypes, we computed their latent-space low-dimensional brain-symptom associations, and used these components to identify phenotypes using unsupervised machine learning. We identified five stable depression phenotypes that were characterized by unique symptom profiles and distinct spectral patterns. Our results demonstrate new neural underpinnings of depression heterogeneity and reveal unique neural phenotypes with potential personalized diagnostic value.

neuroscience↗

bioRxiv2025

Phylogeny-aware comparative genomics of Vibrio vulnificus links genetic traits to pathogenicity

Vibrio vulnificus is a natural inhabitant of coastal brackish waters worldwide and an opportunistic pathogen that can cause severe infections and septicemia through seafood consumption or wound exposure. Due to global warming, its abundance is increasing at high latitudes. While the species harbors diverse virulence factors, its precise disease mechanisms remain unclear. Comparative genomics between clinical and environmental isolates can help identify key virulence genes, but the limited availability of genomes from environmental isolates has hindered progress. In this study, we sequenced the genomes of 82 V. vulnificus isolates from water, sediment, and seagrass along the Baltic Sea coast and complemented with published genomes from 208 clinical and 117 globally distributed isolates for comparative analysis. Phylogenetic reconstruction confirmed four major lineages, with Baltic Sea strains confined to lineage L2 and L4, while clinical and environmental strains were distributed across all lineages. This suggests that the phylogenetic structure of V. vulnificus reflects adaptation to environmental conditions rather than pathogenicity. Using the PhyloBOTL pipeline developed here, we identified 128 orthologs significantly enriched in clinical isolates, grouped into 36 co-localization clusters based on proximity in the genomes. These included genes linked to virulence, such as those for capsular polysaccharide synthesis and biofilm formation, as well as previously unrecognized candidates, including chaperone-usher pilus biosynthesis, spermidine synthesis, Type VI secretion effectors, and an RTX toxin-like protein. Several of the clinically enriched gene clusters have been independently lost in three V. vulnificus clades, suggesting convergent evolution and a distinct ecological niche shared by these claded. Finally, we used the clinically enriched genes to design PCR primers for detecting and monitoring pathogenic V. vulnificus strains, providing a valuable tool for surveillance and public health efforts.

microbiology↗

bioRxiv2024

Distinct bacterial and protist plankton diversity dynamics uncovered through DNA-based monitoring in the Baltic Sea area

Planktonic microorganisms in coastal waters form the base of food web and biogeochemical cycles. The Baltic Sea area, with its pronounced environmental gradients, serves as a model coastal environment. Yet, microbial diversity assessment across these environmental gradients has so far lacked either taxonomic scope or the integration of spatial and temporal scales. Here, we analyzed protist and bacterial diversity using DNA metabarcoding across 398 samples synchronized with national monitoring of the Baltic Sea and the Kattegat-Skagerrak. We show that salinity, unlike other environmental factors, had a stronger effect on bacterial than on protist community composition. Likewise, Bayesian modeling showed that bacterial lineages were less likely than protists to occur in both lower (<9 PSU) and higher (>15 PSU) brackish salinities. Nonetheless, protist alpha diversity increased with salinity. Changes in bacterial alpha diversity were primarily seasonal and linked to influx of deepwater taxa through vertical mixing in winter. We propose that protists are ecologically less sensitive to salinity because compartmentalization allows them to disconnect basic metabolic processes from the cell membrane. Additionally, further and more frequent dispersal of bacteria might impede local adaptation. Ultimately, DNA-based environmental monitoring expands our understanding of microbial diversity patterns and the underlying factors.

microbiology↗

bioRxiv2024

Community stability increases the predictability of microeukaryote community coalescence outcomes

Mixing of entire microbial communities represents a frequent, yet understudied phenomenon. Here, we mimicked estuarine condition in a microcosm experiment by mixing a freshwater river community with a brackish sea community and assessed the effects of both environmental and community coalescences induced by varying mixing processes on microeukaryotic communities. Signs of shifted community composition of coalesced communities towards the sea parent community suggest asymmetrical community coalescence outcome, which, in addition, was generally less impacted by environmental coalescence. Diatoms were negatively impacted by coalescence, while fungi, ciliates, and cercozoans were promoted to varying extents, depending on the mixing ratios of the source (i.e., river or sea) communities. Community stability, inferred from community cohesion, suggests that the more stable parent community (i.e., community with greater negative cohesion attributed to competitive interactions) dominates the final, coalesced community, but the fate of its community members is influenced by mixing ratios and frequencies (i.e., one-time versus repeated coalescence). Generally, community coalescence increased alpha diversity and promoted competition from the introduction (or emergence) of additional (or rare) species. These competitive interactions in turn had community stabilizing effect as evidenced by the increased proportion of negative cohesion. Our study suggests that the predictability of coalescence outcomes was greater when the more stable parent community (i.e., sea microbes) dominated the final community and this predictability was further enhanced when communities collided repeatedly. Open research statementSequencing data is deposited to NCBI SRA database under the accession number PRJNA922225. Data (OTU table, consensus taxonomy and metadata) are available in Open Science Framework (OSF) (http://osf.io/sme36).

ecology↗

bioRxiv2023

Spatial landmark detection and tissue registration with deep learning

Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle non-linear deformations between tissue sections, and are ineffective for z-stack alignment, other modalities beyond image data, or multimodal data. We address these challenges by introducing a new landmark detection and registration method, utilizing neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets, demonstrating superior performance in both accuracy and stability compared to existing approaches.

bioinformatics↗

bioRxiv2023

Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10X Genomics capable of mapping hundreds of transcripts in situ at a subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore eight preview Xenium datasets of the mouse brain and two of human breast cancer by comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies. In addition, we benchmarked the performance of multiple open source computational tools when applied to Xenium datasets in tasks including cell segmentation, segmentation-free analysis, selection of spatially variable genes and domain identification, among others. This study serves as the first independent analysis of the performance of Xenium, and provides best-practices and recommendations for analysis of such datasets.

bioinformatics↗

bioRxiv2022

Points2regions: Fast Interactive Clustering Of In Situ Transcriptomics Data

Imaging-based spatial transcriptomics techniques generate image data that, once processed, results in a set of spatial points with categorical labels for different mRNA species. A crucial part of analyzing downstream data involves the analysis of these point patterns. Here, biologically interesting patterns can be explored at different spatial scales. Molecular patterns on a cellular level would correspond to cell types, whereas patterns on a millimeter scale would correspond to tissue-level structures. Often, clustering methods are employed to identify and segment regions with distinct point-patterns. Traditional clustering techniques for such data are constrained by reliance on complementary data or extensive machine learning, limiting their applicability to tasks on a particular scale. This paper introduces Points2Regions, a practical tool for clustering spatial points with categorical labels. Its flexible and computationally efficient clustering approach enables pattern discovery across multiple scales, making it a powerful tool for exploratory analysis. Points2Regions has demonstrated efficient performance in various datasets, adeptly defining biologically relevant regions similar to those found by scale-specific methods. As a Python package integrated into TissUUmaps and a Napari plugin, it offers interactive clustering and visualization, significantly enhancing user experience in data exploration. In essence, Points2Regions presents a user-friendly and simple tool for exploratory analysis of spatial points with categorical labels.

bioinformatics↗

bioRxiv2022

TissUUmaps 3 Tools for Visualization & Quality Control in Large-scale Multiplex Tissue Analysis

Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in e.g. tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.

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

Decoding spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin

The human spinal cord contains diverse cell types, governed by a series of spatiotemporal events for tissue assembly and functions. However, the spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. Single-cell RNA sequencing and spatial transcriptomics techniques have advanced the understanding of human organ development considerably. By performing integrated analysis of single-cell and spatial multi-omics methods, we created a comprehensive developmental cell atlas of the first trimester human spinal cord. Our data revealed that the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. Beyond this resource, we unexpectedly discovered unique events in human spinal cord development compared to rodents, including earlier quiescence of active neural stem cells, different regulation of stem cell differentiation, and distinct spatiotemporal genetic regulations of cell fate choices. In addition, using our atlas we identified specific gene expression in cancer stem cells in ependymomas. Thus, we demonstrate spatiotemporal genetic regulation of human spinal cord development as well as its potential to understand novel disease mechanisms and to inspire new therapies.

developmental biology↗

bioRxiv2022

Co-occurrences enhance our understanding of aquatic fungal metacommunity assembly and reveal potential host-parasite interactions

Our knowledge of aquatic fungal communities, their assembly, distributions and ecological roles in marine ecosystems is scarce. Hence, we aimed to investigate fungal metacommunities of coastal habitats in a subarctic zone (northern Baltic Sea, Sweden). Using a novel joint species distribution model and network approach, we quantified the importance of biotic associations contributing to the assembly of mycoplankton, further, detected potential biotic interactions between fungi-algae pairs, respectively. Our long-read metabarcoding approach identified 504 fungal taxa, of which a dominant fraction (44.8 %) was assigned as early-diverging fungi (i.e., Cryptomycota and Chytridiomycota). Alpha diversity of mycoplankton declined and community compositions changed along inlet-bay- offshore transects. The distributions of most fungi were rather influenced by spatial factors than by environmental drivers, and the influence of biotic associations was pronounced when environmental filtering was weak and spatial patterning lessened. We found great number of co-occurrences (138) among the dominant fungal groups, and the forty associations between fungal and algal OTUs suggested potential host-parasite/saprotroph links, supporting a Cryptomycota-based mycoloop pathway. We emphasize that the contribution of biotic associations to mycoplankton assembly are important to consider in future studies as it helps to improve predictions of species distributions in aquatic ecosystems.

ecology↗

bioRxiv2022

Markov State Modelling Reveals Heterogeneous Drug-Inhibition Mechanism of Calmodulin

Calmodulin (CaM) is a calcium sensor which binds and regulates a wide range of target-proteins. This implicitly enables the concentration of calcium to influence many downstream physiological responses, including muscle contraction, learning and depression. The antipsychotic drug trifluoperazine (TFP) is a known CaM inhibitor. By binding to various sites, TFP prevents CaM from associating to target-proteins. However, the molecular and state-dependent mechanisms behind CaM inhibition by drugs such as TFP are largely unknown. Here, we build a Markov state model (MSM) from adaptively sampled molecular dynamics simulations and reveal the structural and dynamical features behind the inhibitory mechanism of TFP-binding to the C-terminal domain of CaM. We specifically identify three major TFP binding-modes from the MSM macrostates, and distinguish their effect on CaM conformation by using a systematic analysis protocol based on biophysical descriptors and tools from machine learning. The results show that depending on the binding orientation, TFP effectively stabilizes features of the calcium-unbound CaM, either affecting the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the bound domain. The conclusions drawn from this work may in the future serve to formulate a complete model of pharmacological modulation of CaM, which furthers our understanding of how these drugs affect signaling pathways as well as associated diseases. Author summaryCalmodulin (CaM) is a calcium-sensing protein which makes other proteins dependent on the surrounding calcium concentration by binding to these proteins. Such protein-protein interactions with CaM are vital for calcium to control many physiological pathways within the cell. The antipsychotic drug trifluoperazine (TFP) inhibits CaMs ability to bind and regulate other proteins. Here, we use molecular dynamics simulations together with Markov state modeling and machine learning to understand the structural and dynamical features by which TFP bound to the one domain of CaM prevents association to other proteins. We find that TFP encourages CaM to adopt a conformation that is like the one stabilized in absence of calcium: depending on the binding orientation of TFP, the drug indeed either affects the CaM hydrophobic binding pocket, the calcium binding sites or the secondary structure content in the domain. Understanding TFP binding is a first step towards designing better drugs targeting CaM.

biophysics↗

bioRxiv2022

TissUUmaps 3: Interactive visualization and quality assessment of large-scale spatial omics data

Background and ObjectivesSpatially resolved techniques for exploring the molecular landscape of tissue samples, such as spatial transcriptomics, often result in millions of data points and images too large to view on a regular desktop computer, limiting the possibilities in visual interactive data exploration. TissUUmaps is a free, open-source browser-based tool for GPU-accelerated visualization and interactive exploration of 107+ data points overlaying tissue samples. MethodsHerein we describe how TissUUmaps 3 provides instant multiresolution image viewing and can be customized, shared, and also integrated into Jupyter Notebooks. We introduce new modules where users can visualize markers and regions, explore spatial statistics, perform quantitative analyses of tissue morphology, and assess the quality of decoding in situ transcriptomics data. ResultsWe show that thanks to targeted optimizations the time and cost associated with interactive data exploration were reduced, enabling TissUUmaps 3 to handle the scale of todays spatial transcriptomics methods. ConclusionTissUUmaps 3 provides significantly improved performance for large multiplex datasets as compared to previous versions. We envision TissUUmaps to contribute to broader dissemination and flexible sharing of large-scale spatial omics data.

bioinformatics↗

bioRxiv2022

Dopamine and ALK4 signaling synergize to induce PCBP1-mediated alternative splicing of FosB and sustained behavioral sensitization to cocaine

{Delta}FosB, an alternative spliced product of FosB, is an essential component of dopamine-induced reward pathways and a master switch for addiction. However, the molecular mechanisms of its generation and regulation by dopamine signaling are unknown. Here we report that dopamine D1 receptor signaling synergizes with the activin/ALK4/Smad3 pathway to potentiate the generation of {Delta}FosB mRNA in medium spiny neurons (MSNs) of the nucleus-accumbens (NAc) through activation of the RNA binding protein PCBP1, a regulator of mRNA splicing. Concurrent activation of PCBP1 and Smad3 by D1 and ALK4 signaling induced their interaction, nuclear translocation, and binding to sequences in exon-4 and intron-4 of FosB mRNA. Ablation of either ALK4 or PCBP1 in MSNs impaired {Delta}FosB mRNA induction and nuclear translocation of {Delta}FosB protein in response to repeated co-stimulation of D1 and ALK4 receptors. Importantly, ALK4 was required in NAc MSNs of adult mice for behavioral sensitization to cocaine. These findings uncover an unexpected mechanism for {Delta}FosB generation and drug-induced sensitization through convergent dopamine and ALK4 signaling.

neuroscience↗

bioRxiv2021

AraC binds the p75NTR transmembrane domain to induce neurodegeneration in mature neurons.

BackgroundCytosine arabinoside (AraC) is one of the main therapeutic treatments for several types of cancer including acute myeloid leukaemia. However, after high dose AraC chemotherapy regime, patients develop severe neurotoxicity and neurodegeneration in the central nervous system leading to cerebellar ataxia, dysarthria, nystagmus, somnolence and drowsiness. AraC induces apoptosis in dividing cells, however, the mechanism by which it leads to neurite degeneration and cell death in mature neurons remains unclear. We hypothesized that the upregulation of the death receptor p75NTR is responsible for AraC-mediated neurodegeneration and cell death in leukemia patients undergoing AraC treatment. MethodsTo determine the role of AraC-p75NTR signalling in degeneration of mature cerebellar granule neurons, we used primary cultures from p75NTR knockout and p75NTRCys259 mice. Evaluation of neurodegeneration, cell death and p75NTR signalling was done by immunohistochemistry and immunoblotting. To assess the direct interaction between AraC and p75NTR, we performed isothermal dose response-cellular thermal shift and AraTM assays as well as Homo-FRET anisotropy imaging. ResultsWe show that AraC induces neurite degeneration and programmed cell death of mature cerebellar granule neurons in a p75NTR-dependent manner. Mechanistically, AraC binds to Proline 252 and Cysteine 256 of the p75NTR transmembrane domain and selectively uncouples p75NTR from the NF{kappa}B survival pathway. This in turn, exacerbates the activation of the cell death/JNK pathway by recruitment of TRAF6 to p75NTR. ConclusionOur findings identify p75NTR as a novel molecular target to develop treatments to counteract AraC-mediated neurodegeneration.

cell biology↗