Biology subjects

Fernandez-Albert, F.

Publications and source records attributed to Fernandez-Albert, F..

5 recordsLinked to original sources

bioRxiv2026

singIST: an R/Bioconductor library and Quarto dashboard for automated single-cell comparative transcriptomics analysis ofdisease models and humans

Preclinical disease models often diverge from human pathophysiology at single-cell resolution, complicating model selection and limiting translational value. We present singIST, an R/Bioconductor package for quantitative and explainable comparison of disease model scRNA-seq data against a human reference. For each superpathway, singIST fits an adaptive sparse multi-block PLS-DA model on human pseudobulk expression, integrated one-to-one orthology and cell type mapping, and translates model fold changes into the human expression space to compute signed recapitulation at the superpathway, cell type, and gene levels. To streamline interpretation and reporting, we provide singIST Visualizer, a companion Quarto/Shiny dashboard that loads singIST outputs and offers interactive exploration with export ready plots and tables, avoiding manual figure coding across many superpathways and models. We demonstrate the workflow. We illustrate an end-to-end workflow on an oxazolone mouse model against a human atopic dermatitis reference for two representative pathways: Dendritic Cells in regulating Th1/Th2 Development [BIOCARTA] and Cytokine-cytokine receptor interaction [KEGG]. singIST is distributed under the MIT License via Bioconductor, and the Visualizer is available on GitHub.

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bioRxiv2024

singIST: an integrative method for comparative single-cell trancriptomics between disease models and humans

MotivationDisease models serve as fundamental tools in drug discovery and early-stage drug development. However, these models are not a perfect reflection of human disease, and selecting a suitable model can be challenging. Existing computational approaches for molecular validation of pathophysiological resemblance to human conditions at single-cell resolution remain limited. Although quantitative computational methods exist to inform this selection, they are very limited at the single-cell resolution, which can be critical for model selection. Quantifying the resemblance of disease models to the human condition with single-cell technologies in an explainable, integrative, and generalizable manner remains a significant challenge. ResultsWe present singIST, a computational method for comparative single-cell transcriptomics analysis between disease models and human conditions. singIST provides explainable quantitative measures on disease model similarity to human condition at both pathway and cell type levels, highlighting the importance of each gene in the latter. These measures account for orthology, cell type presence in the disease model, cell type and gene importance in human condition, and gene changes in the disease model measured as fold change. This is achieved within a unifying framework that controls for the intrinsic complexities of single-cell data. We tested our method using three well-characterized murine models of moderate-to-severe Atopic Dermatitis, demonstrating its ability to recapitulate established biological knowledge while generating novel hypothesis through pathway-level analysis. Availability and implementationR library at https://github.com/DataScienceRD-Almirall/singIST and implementation code at https://github.com/DataScienceRD-Almirall/singIST_paper_results Author SummaryIn this study, we set out a method to improve how researchers evaluate disease models - tools that are essential for understanding human illness and developing new treatments. A major limitation of current methods is that they overlook the fine-scale differences between disease models and human conditions at the single-cell level. Yet, many diseases are driven by changes that occur in specific cell types. To address this, we developed singIST, a method that allows us to compare disease models to human diseases using single-cell gene expression data. What makes singIST unique is its ability to provide clear, interpretable measures of similarity between a disease model and the human condition, specifically at the level of cell types and groups of functionally related genes. We demonstrated the method using three well-characterized mouse models of atopic dermatitis and showed that singIST could both reflect known disease biology and uncover new insights into disease mechanisms. Further, we developed statistical tests to validate the trained models. Our approach is flexible and broadly applicable, making it a valuable tool for improving the selection and understanding of disease models in biomedical research.

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bioRxiv2024

In Silico Treatment: a computational framework for animal model selection and drug assessment

The translation of findings from animal models to human disease is a fundamental part in the field of drug development. However, only a small proportion of promising preclinical results in animals translate to human pathophysiology. This underscores the necessity for novel data analysis strategies to accurately evaluate the most suitable animal model for a specific purpose, ensuring cross-species translatability. To address this need, we present In Silico Treatment (IST), a computational method to assess translation of disease-related molecular expression patterns between animal models and humans. By simulating changes observed in animals onto humans, IST provides a holistic picture of how well animal models recapitulate key aspects of human disease, or how treatments transform pathogenic expression patterns to healthy ones. Furthermore, IST highlights particular genes that influence molecular features of pathogenesis or drug mode of action. We demonstrate the potential of IST with three applications using bulk transcriptomics data. First, we assessed two mouse models for idiopathic pulmonary fibrosis (IPF): one involving injury with intra-tubular Bleomycin exposure, and the other Adeno-associated-virus-induced, TGF{beta}1-mediated tissue transformation (AAV6.2-TGF{beta}1). Both models exhibited gene expression patterns resembling extracellular matrix derangement in human IPF, whereas differences in VEGF-driven vascularization were observed. Second, we confirmed known features of non-alcoholic steatohepatitis (NASH) mouse models, including choline-deficient, l-amino acid-defined diet (CDAA), carbon tetrachloride hepatotoxicity injury (CCl4) and bile duct ligation surgery (BDL). Overall, the three mouse models recapitulated expression changes related to fibrosis in human NASH, whereas model-specific differences were found in lipid metabolism, inflammation, and apoptosis. Third, we reproduced the strong anti-fibrotic signature and induction of the PPAR signaling observed in the Elafibranor experimental treatment for NASH in the CDAA model. We validated the contribution of known disease-related genes to the findings made with IST in the IPF and NASH applications. The complete data integration IST framework, including an interactive app to integrate and compare datasets, is made available as an open-source R package. Author summaryPreclinical testing plays a pivotal role in the drug development process, serving as a crucial evaluation phase before a new drug can be tested on humans in clinical trials. The drug must undergo a rigorous evaluation in in vivo and in vitro preclinical studies to assess its safety and efficacy. However, positive outcomes in preclinical animal models do not always translate to positive results in humans, mainly due to biological differences. Therefore, selecting an animal model that closely mirrors human disease traits and detecting and accounting for model limitations is of paramount importance. Over the last decade, the availability of gene expression data in both animals and humans has substantially increased. Gene expression states and perturbations are routinely employed as a proxy to predict and understand changes in disease states. Here, we developed In Silico Treatment, a computational method designed to overlay the gene expression changes observed in animals onto humans, quantifying the change in human disease status. We applied this method to mouse models for idiopathic pulmonary fibrosis and non-alcoholic steatohepatitis, two severe fibrotic diseases. We successfully identified known features of the disease models and provide a granular gene-level rationale behind our predictions. Consequently, our method shows promise as an effective approach to improve animal model selection and thus clinical translation.

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bioRxiv2023

BrainTACO: An Explorable Multi-Scale Multi-Modal Brain Transcriptomic And Connectivity Data Resource

AbstractExploring the relationships between genes, brain circuitry, and behaviour is accelerated by the joint analysis of a heterogeneous sets form 3D imaging data, anatomical data, and brain networks at varying scales, res-olutions, and modalities. Hence, generating an integrated view, beyond the individual resources original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few plat-forms for integration and exploration of this heterogeneous data exist. To this end, we present the BrainTACO (Brain Transcriptomic And Connectivity Data) resource, a selection of heterogeneous, and multi-scale neurobiological data spatially mapped onto a common, hierarchical reference space, combined via a holistic data integration scheme. To access BrainTACO, we extended BrainTrawler, a web-based visual ana-lytics framework for spatial neurobiological data, with comparative visualizations of multiple resources for gene expression dissection of brain networks with an unprecedented coverage. Using this platform, allows to straightforward explore and extract brain data for identifying potential genetic drivers of connectivity in both mice and humans that may contribute to the discovery of dysconnectivity phenotypes. Hence, BrainTACO reduces the need for time-consuming manual data aggregation often required for computational analyses in script based toolboxes, and supports neuroscientists by focusing on leveraging the data instead of preparing it.

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bioRxiv2020

HieRFIT: Hierarchical Random Forest for Information Transfer

The emergence of single-cell RNA sequencing (scRNA-seq) has led to an explosion in novel methods to study biological variation among individual cells, and to classify cells into functional and biologically meaningful categories. Here, we present a new cell type projection tool, HieRFIT (Hierarchical Random Forest for Information Transfer), based on hierarchical random forests. HieRFIT uses a priori information about cell type relationships to improve classification accuracy, taking as input a hierarchical tree structure representing the class relationships, along with the reference data. We use an ensemble approach combining multiple random forest models, organized in a hierarchical decision tree structure. We show that our hierarchical classification approach improves accuracy and reduces incorrect predictions especially for inter-dataset tasks which reflect real life applications. We use a scoring scheme that adjusts probability distributions for candidate class labels and resolves uncertainties while avoiding the assignment of cells to incorrect types by labeling cells at internal nodes of the hierarchy when necessary. Using HieRFIT, we re-analyzed publicly available scRNA-seq datasets showing its effectiveness in cell type cross-projections with inter/intra-species examples. HieRFIT is implemented as an R package and it is available at (https://github.com/yasinkaymaz/HieRFIT/releases/tag/v1.0.0)

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