Biology subjects

Papatheodorou, I.

Publications and source records attributed to Papatheodorou, I..

At least 19 recordsLinked to original sources

bioRxiv2026

FlyAnatoView: Visualization of Drosophila Single-cell RNA-seq Datasets on Anatomographs

Single-cell RNA sequencing (scRNA-seq) is a powerful approach for measuring the transcriptomic landscape at single-cell resolution. However, while scRNA-seq data reveals what genes each cell expresses, they do not indicate where that cell was located within the tissue or organ, anatomically. We developed FlyAnatoView (https://www.flyrnai.org/tools/rna_seq_base/web/svg_files/), a resource that projects scRNA-seq data onto anatomical diagrams, restoring this missing spatial context.

bioinformatics↗

bioRxiv2026

EISCA and EISTA: Full-Spectrum Pipelines for Single-Cell and Spatial Transcriptomics Analysis

Single-cell and spatial transcriptomics are transforming our understanding of cellular heterogeneity and tissue organization, yet their analytical complexity remains a major bottleneck. Here, we present EISCA and EISTA, two standardized, end-to-end pipelines for single-cell RNA-seq and imaging-based spatial transcriptomics analysis. Built on the Nextflow nf-core framework, both pipelines implement modular, scalable, and reproducible workflows spanning primary, secondary, and tertiary analyses, from raw data processing to advanced downstream analyses. EISCA supports droplet- and plate-based scRNA-seq technologies, while EISTA is tailored for high-resolution spatial platforms including Vizgen MERFISH and 10x Xenium. Together, they integrate state-of-the-art methods for quality control, normalization, clustering, integration, cell-type annotation, differential expression, and cell-cell communication, with EISTA further enabling spatial statistical analyses. A central design principle is to balance standardization with flexibility: workflows can be executed end-to-end or modularly, enabling iterative, exploratory analyses with minimal overhead. Both pipelines deliver rapid preliminary results alongside an out-of-the-box report, facilitating immediate data assessment and accelerating downstream discovery. Case studies in plant immunity and human sepsis demonstrate that EISTA and EISCA reproducibly can be used to recover biologically meaningful insights. Collectively, these pipelines provide efficient, flexible, and scalable solutions for comprehensive single-cell and spatial transcriptomics analyses.

bioinformatics↗

bioRxiv2025

scDenorm: a denormalisation tool for integrating single-cell transcriptomics data

Integrating single-cell omics data at an atlas scale enhances our understanding of cell types and disease mechanisms. However, the integration of data processed by different normalisation methods can lead to biases, such as unexpected batch effects and gene expression distortion, leading to misinterpretations in downstream analysis. To address these challenges, we present scDenorm, an algorithm that reverts normalised single-cell omics data to raw counts, preserving the integrity of the original measurements and ensuring consistent data processing during integration. We evaluated scDenorms performance on large-scale datasets and benchmarked its impact on data integration and downstream analysis across three datasets.

bioinformatics↗

bioRxiv2025

Decoding MASLD Progression: A Molecular Trajectory-Based Framework for Modelling Disease Dynamics

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) has emerged as a silent pandemic, affecting nearly one-third of the global population. MASLD encompasses a spectrum of liver disorders, ranging from simple steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), characterised by lipotoxicity, hepatocellular injury, inflammation, and fibrosis, which can eventually progress to cirrhosis and hepatocellular carcinoma. Despite the progressive nature of MASLD/MASH, current research and clinical practice primarily rely on static, histopathology-defined stages that fail to capture the continuous nature of disease progression. Here, we present an integrative framework that combines patient pseudo-temporal ordering, network analysis, and cell-type deconvolution to reconstruct the continuous MASLD/MASH trajectory. By analysing patient liver transcriptomic profiles, we position patients along this data-driven trajectory, moving beyond conventional stage-based classifications. This approach reveals the sequence of critical molecular events underlying MASLD/MASH progression, providing mechanistic insights into the diseases pathophysiology. By integrating these findings with plasma proteomics data, we identify novel trajectory-specific plasma biomarkers that predict disease stage (and trajectory position) independently of histology. Together, these findings demonstrate the value of trajectory-based frameworks for understanding MASLD pathophysiology and highlight new opportunities for precision diagnosis and therapeutic target prioritisation across the disease spectrum.

systems biology↗

bioRxiv2024

Long-term immune changes after COVID-19 and the effect of BCG vaccination and latent infections on disease severity

BackgroundSeveral years after the COVID-19 pandemic, the role of trained immunity in COVID-19 remains controversial, and questions regarding the long-term effects of COVID-19 on immune cells remain unresolved. We investigated the roles of Bacillus Calmette-Guerin (BCG) vaccination and latent infections in the progression of COVID-19 and sepsis. MethodsWe conducted a prospective analysis of 97 individuals recovering from mild-to-critical COVID-19 and 64 sepsis patients. Immune cell frequencies, expression of functional markers, and plasma titres of anti-Toxoplasma gondii/cytomegalovirus/BCG antibodies were assessed and their impact on disease severity and outcomes were determined. To examine monocyte responses to secondary challenge, monocytes isolated from COVID-19 convalescent patients, BCG vaccinated and unvaccinated volunteers were stimulated with SARS-CoV-2 and LPS. ResultsPost COVID-19 patients showed immune dysregulation regardless of disease severity characterized mainly by altered expression of activation and functional markers in myeloid (CD39, CD64, CD85d, CD11b) and lymphoid cells (CD39, CD57, TIGIT). Strikingly, post-critical COVID-19 patients showed elevated expression of CD57 in CD8+ T cells compared to other severity groups. Additionally, a higher frequency of CMV and T. gondii seropositive-alongside a lower frequency of BCG seropositive-patients were associated with severe and critical COVID-19. However, the monocyte response to stimulation was unaffected by the severity of COVID-19. ConclusionThese findings highlight the long-term alterations of immune cells in post-COVID-19 patients emphasizing the substantial impact of COVID-19 on immune function. However, our data showed no relationship between previous BCG vaccination and protection against SARS-CoV-2 infection.

immunology↗

bioRxiv2024

Context-aware comparison of cell type gene expression across species

The cross-species comparison of expression profiles uncovers functional similarities and differences between cell types and helps refining their evolutionary relationships. Current analysis strategies typically follow the ortholog conjecture, which posits that the expression of orthologous genes is most similar between species. However, the extent to which this holds true at different evolutionary distances is unknown. Here, we systematically explore the ortholog conjecture in comparative scRNA-seq data. We devise a robust analytical framework, GeneSpectra, to classify genes by expression specificity and distribution across cell types. Our analysis reveals that genes expressed ubiquitously across nearly all cell types exhibit strong conservation of this pattern across species, as do genes with high expression specificity. In contrast, genes within intermediate specificity fluctuate between classes. As expected, ortholog expression becomes more divergent with increased species distance. We also find an overall correlation between similarity in expression profiles and sequence conservation. Finally, our results allow identifying gene classes with highest probability of expression pattern conservation that are most useful for cell type alignment between species. Calibrating reliance on the ortholog conjecture for individual genes, we thus provide a comprehensive framework for the comparative analysis of single cell data.

bioinformatics↗

bioRxiv2024

Unveiling the guardians: IL-26-expressing MAIT cells protect epithelial barrier function and are dysregulated in Crohn's disease

BACKGROUND & AIMSInflammatory bowel disease (IBD) is characterized by a dysregulated immune response against the hosts microbiome. Mucosal-associated invariant T (MAIT) cells recognize microbiota-derived riboflavin metabolites and play a crucial role in mucosal homeostasis. However, their specific role in IBD remains enigmatic. MAIT cells express IL-26, a novel IL-10 family cytokine with a controversial role in IBD. We investigated the functions of MAIT cells and IL-26 in IBD using a unique combination of state-of-the-art 3D human intestinal tissue models and clinical samples. METHODSWe analyzed MAIT cells from the peripheral blood and intestinal tissue of Crohns disease (CD) patients, using immunofluorescence staining and flow cytometry to describe the phenotype and IL-26 expression of MAIT cells. We used 3D iPSC-derived intestinal organoids as a complex in vitro model of human tissue and RNA sequencing and functional assays such as wound healing assay to study the role of IL-26 in mucosal homeostasis and inflammation. RESULTSWe observed a reduction of MAIT cells in the peripheral blood of CD patients compared to healthy donors (1.5 {+/-} 0.4%; 4.1 {+/-} 1.1%; p < .0065) and a significant decrease of MAIT cells in inflamed compared to non-inflamed ileum of CD patients (0.1 {+/-} 0.03%; 0.17 {+/-} 0.05%; p < .042). MAIT cells were found pathologically activated in inflamed tissue, exhibiting differences in CD8 and CD4 expression and dysregulation of IL-26 expression. Furthermore, we demonstrated a protective role of IL-26 in mucosal homeostasis and inflammation in the iPSC-derived organoid model. CONCLUSIONOur results show a crucial role for IL-26 and MAIT cells in the homeostasis of intestinal tissue and in the pathogenesis of IBD. These cells may therefore represent new therapeutic targets for CD patients.

immunology↗

bioRxiv2024

Serotonin attenuates tumor-necrosis-factor-alpha-induced intestinal inflammation by interacting with human mucosal tissue

The intestine houses the largest reservoir of immune cells and is serviced by the largest and most complex peripheral nervous system in the human body. The gut-brain axis orchestrates bidirectional communication between the central and enteric nervous systems, playing a pivotal role in regulating overall body function and intestinal homeostasis. Using a human 3D in vitro model, we investigated the effect of serotonin, a neuromodulator produced in the gut, on immune cell and intestinal tissue interactions. Our findings revealed that serotonin attenuates the tumor-necrosis-factor-alpha-induced pro-inflammatory response, mostly by affecting the expression of chemokines. Serotonin was found to impact tissue-migrating monocytes phenotype and distribution, without direct contact with the cells, by remodeling the intestinal tissue. Collectively, using fully human 3D model of intestine, our results show for the first time that serotonin has a crucial role in communication among gut-brain axis components and regulates monocyte migration and plasticity, thereby contributing to gut homeostasis and the progression of intestinal inflammation. In vivo studies focused on role of neuromodulators in gut homeostasis and inflammation have shown controversial results, highlighting importance of development of human experimental models. Moreover, our results emphasize importance of human health research in human-cell-based models and suggests serotonin signaling pathway as new potential therapeutic target for inflammatory bowel disease patients.

immunology↗

bioRxiv2024

ScGOclust: leveraging gene ontology to compare cell types across distant species using scRNA-seq data

Basic biological processes are shared among animal species, yet their cellular mechanisms are profoundly diverse. Comparing cell type expression profiles across species reveals the conservation and divergence of cellular functions. With the increase of phylogenetic distance between species of interest, a gene-based comparison becomes limited. The Gene Ontology (GO) knowledgebase is the most comprehensive resource of gene functions, providing a bridge for comparing cell types between remote species. Here, we present scGOclust, a computational tool to construct cellular functional profiles using GO terms and facilitates systematic, robust comparisons within and across species. We use scGOclust to analyse and compare the heart, gut and kidney between mouse and fly. We show that scGOclust recapitulates the function spectrum of different cell types, characterises functional similarities between homologous cell types, and reveals functional convergence between unrelated cell types. Furthermore, we identify subpopulations in the fly crop by cross-species comparison of GO profiles. Finally, scGOclust resolved the analogy between Malpighian tubule and kidney segments.

bioinformatics↗

bioRxiv2023

Integrated analysis of baseline protein expression in pig tissues

The availability of an increasingly large amount of public proteomics datasets presents an opportunity for performing combined analyses to generate comprehensive organism-wide protein expression maps across different organisms and biological conditions. Sus scrofa, the domestic pig, is a model organism relevant for food production and for human biomedical research. Here we reanalyzed 14 public proteomics datasets from the PRIDE database coming from pig tissues to assess baseline (without any biological perturbation) protein abundance in 14 organs, encompassing a total of 20 healthy tissues from 128 samples. The analysis involved the quantification of protein abundance in 599 mass spectrometry runs. We compared protein expression patterns among different pig organs and examined the distribution of proteins across these organs. Then, we studied how protein abundances compared across different datasets and studied the tissue specificity of the detected proteins. Of particular interest, we conducted a comparative analysis of protein expression between pig and human tissues, revealing a high degree of correlation in protein expression among orthologs, particularly in brain, kidney, heart, and liver samples. We have integrated the protein expression results into the Expression Atlas resource for easy access and visualisation of the protein expression data individually or alongside gene expression data.

bioinformatics↗

bioRxiv2023

Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution

Cancer is a highly heterogeneous disease, where phenotypically distinct subpopulations coexist and could be primed to different fates. Both genetic and epigenetic factors may drive cancer evolution, however little is known about whether and how such a process is pre-encoded in cancer clones. Using single-cell multi-omic lineage tracing and phenotypic assays, we investigate the predictive features of either tumour initiation or drug tolerance within the same cancer population. Clones primed to tumour initiation in vivo display two distinct transcriptional states at the baseline. Remarkably, these states share a distinctive DNA accessibility profile, highlighting an epigenetic basis for tumour initiation. The drug tolerant niche is also largely pre-encoded, but only partially overlaps the tumour-initiating one and evolves following two genetically and transcriptionally distinct trajectories. Our study highlights coexisting genetic, epigenetic and transcriptional determinants of cancer evolution, unravelling the molecular complexity of pre-encoded tumour phenotypes.

genomics↗

bioRxiv2023

Power analysis of cell-type deconvolution methods across tissues

Cell-type deconvolution methods aim to infer cell composition from bulk transcriptomic data. The proliferation of developed methods, coupled with inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Additionally, the growing accessibility of single-cell RNA sequencing datasets, often accompanied by bulk expression from related samples, enable the benchmark of existing methods. In this study, we conduct a comprehensive assessment of 31 methods, utilising single-cell RNA-sequencing data from diverse human and mouse tissues. Employing various simulation scenarios, we reveal the efficacy of regression-based deconvolution methods, highlighting their sensitivity to reference choices. We investigate the impact of bulk-reference differences, incorporating variables such as sample, study and technology. We provide validation using a gold standard dataset from mononuclear cells and suggest a consensus prediction of proportions when ground truth is not available. We validated the consensus method on data from the stomach and studied its spillover effect. Importantly, we propose the use of the Critical Assessment of Transcriptomic Deconvolution (CATD) pipeline which encompasses functionalities for generating references and pseudo-bulks and running implemented deconvolution methods. CATD streamlines simultaneous deconvolution of numerous bulk samples, providing a practical solution for speeding up the evaluation of newly developed methods. Availability: https://github.com/Papatheodorou-Group/CATD_snakemake

bioinformatics↗

bioRxiv2022

Towards a Clinically-based Common Coordinate Framework for the Human Gut Cell Atlas - The Gut Models

BackgroundThe Human Cell Atlas resource will deliver single cell transcriptome data spatially organised in terms of gross anatomy, tissue location and with images of cellular histology. This will enable the application of bioinformatics analysis, machine learning and data mining revealing an atlas of cell types, sub-types, varying states and ultimately cellular changes related to disease conditions. To further develop the understanding of specific pathological and histopathological phenotypes with their spatial relationships and dependencies, a more sophisticated spatial descriptive framework is required to enable integration and analysis in spatial terms. MethodsWe describe a conceptual coordinate model for the Gut Cell Atlas (small and large intestines). Here, we focus on a Gut Linear Model (1-dimensional representation based on the centreline of the gut) that represents the location semantics as typically used by clinicians and pathologists when describing location in the gut. This knowledge representation is based on a set of standardised gut anatomy ontology terms describing regions in situ, such as ileum or transverse colon, and landmarks, such as ileo-caecal valve or hepatic flexure, together with relative or absolute distance measures. We show how locations in the 1D model can be mapped to and from points and regions in both a 2D model and 3D models, such as a patients CT scan where the gut has been segmented. ResultsThe outputs of this work include 1D, 2D and 3D models of the human gut, delivered through publicly accessible Json and image files. We also illustrate the mappings between models using a demonstrator tool that allows the user to explore the anatomical space of the gut. All data and software is fully open-source and available online. ConclusionsSmall and large intestines have a natural "gut coordinate" system best represented as a 1D centreline through the gut tube, reflecting functional differences. Such a 1D centreline model with landmarks, visualised using viewer software allows interoperable translation to both a 2D anatomogram model and multiple 3D models of the intestines. This permits users to accurately locate samples for data comparison.

bioinformatics↗

bioRxiv2022

Benchmarking strategies for cross-species integration of single-cell RNA sequencing data

The growing number of available single cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. We benchmarked 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examined the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also developed a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieved a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs was beneficial. SAMap outperformed when integrating whole-body atlases between species with challenging gene homology annotation. We provided our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms.

bioinformatics↗

bioRxiv2022

Wnt and BMP signaling enhance melanocyte regeneration and suppress melanoma cell migration

Tissue regeneration and cancer share remarkable features including activation of cell proliferation and migration. Yet, tumors considerably differ from the regenerating tissue with respect to abnormal proliferation, invasive growth, and metastasis. Thus, it is likely that cancer resembles early stages of regeneration with increased proliferation, but separates from the later stages with reduced proliferation and enhanced differentiation. Here, by exploiting the zebrafish melanocytes that can efficiently regenerate and be induced to undergo malignant melanoma, we unravel the transcriptome profiles of the regenerating melanocytes during early and late regeneration, and the melanocytic nevi and malignant melanoma. Our global comparison of the gene expression profiles of melanocyte regeneration and nevi/melanoma uncovers the differential regulation of a substantial number of genes related to Wnt signaling and TGF-{beta}/BMP signaling pathways between regeneration and cancer. Functional activation of canonical Wnt or TGF-{beta}/BMP pathways during melanocyte regeneration promoted melanocyte regeneration and potently suppressed the invasiveness, migration, and proliferation of human melanoma cells in vitro and in vivo. Therefore, differential regulation of signaling mechanisms between regeneration and cancer can be exploited to stop tumor growth and develop new anticancer therapies.

developmental biology↗

bioRxiv2021

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination. Author summaryWe have reanalysed 23 baseline mass spectrometry-based public proteomics datasets stored in the PRIDE database. Overall, the aggregated dataset contained 211 samples, coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. We analysed the distribution of protein expression across organs in both species. We also studied how protein abundances compared across different datasets and organs for both species. Then we performed gene ontology and pathway enrichment analyses to identify enriched biological processes and pathways across organs. We also carried out a comparative analysis of baseline protein expression across mouse, rat and human tissues, observing a high level of expression correlation among orthologs in all three species, in brain, kidney, heart and liver samples. To disseminate these findings, we have integrated the protein expression results into the resource Expression Atlas.

bioinformatics↗

bioRxiv2021

Implementing the re-use of public DIA proteomics datasets: from the PRIDE database to Expression Atlas

The number of mass spectrometry (MS)-based proteomics datasets in the public domain keeps increasing, particularly those generated by Data Independent Acquisition (DIA) approaches such as SWATH-MS. Unlike Data Dependent Acquisition datasets, the re-use of DIA datasets has been rather limited to date, despite its high potential, due to the technical challenges involved. We introduce a (re-)analysis pipeline for public SWATH-MS datasets which includes a combination of metadata annotation protocols, automated workflows for MS data analysis, statistical analysis, and the integration of the results into the Expression Atlas resource. Automation is orchestrated with Nextflow, using containerised open analysis software tools, rendering the pipeline readily available and reproducible. To demonstrate its utility, we reanalysed 10 public DIA datasets from the PRIDE database, comprising 1,278 SWATH-MS runs. The robustness of the analysis was evaluated, and the results compared to those obtained in the original publications. The final expression values were integrated into Expression Atlas, making SWATH-MS experiments more widely available and combining them with expression data originating from other proteomics and transcriptomics datasets.

bioinformatics↗

bioRxiv2021

A proteomics sample metadata representation for multiomics integration, and big data analysis.

The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the users choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data. Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGETAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

bioinformatics↗