Biology subjects

Esteban, M.

Publications and source records attributed to Esteban, M..

5 recordsLinked to original sources

bioRxiv2025

Single-cell Spatial Transcriptomics Reveals Disease-specificMicroenvironmental Niches in Neurodegeneration and COVID-19

Neurodegenerative diseases and infections can produce lasting effects on brain function, yet the spatial molecular mechanisms underlying these changes remain poorly understood. Here, we present high-resolution spatial transcriptomics of 40 postmortem brain samples from patients with Parkinsons disease, frontotemporal dementia, dementia with Lewy bodies, and severe COVID-19. Analyzing over 1.5 million spatially resolved cells across dorsolateral prefrontal cortex and anterior cingulate cortex revealed disease-specific transcriptional signatures with pronounced layer-and region-specificity. In Parkinsons disease, we identified stressed neurons creating distinctive microenvironmental gradients where metabolic and protein degradation pathways are elevated near stress epicenters, while regenerative processes increase with distance. COVID-19 brains displayed extensive peripheral immune cell infiltration, particularly in the subcortical white matter, accompanied by compromised blood-brain barrier and coordinated neuroinflammatory responses from microglia, astrocytes, and endothelial cells. Integration of miRNA sequencing with spatial transcriptomics uncovered layer-specific regulatory patterns, including neuroinflammation-associated miR-155. This atlas provides unprecedented insights into disease pathology and highlights the critical importance of spatial molecular context in understanding brain disorders. Key Messages[1] A high-resolution single-cell spatial transcriptomics atlas of the dorsolateral prefrontal cortex and anterior cingulate cortex across neurodegenerative conditions and severe COVID-19 [2] Region-and layer-specific transcriptional dysregulation across disease comparisons reveals disease-specific differential vulnerability [3] Metabolically stressed cells found selectively in the anterior cingulate cortex of Parkinsons disease patients but not in dementia with Lewy bodies, along with detailed characterization of their spatial microenvironment [4] Peripheral immune cell clusters identified in the white matter of the cerebral cortex of COVID-19 patients, with detailed characterization of their spatial microenvironment [5] Integration of bulk miRNA sequencing reveals cortical layer-specific miRNA regulatory patterns

neuroscience↗

bioRxiv2021

Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis

Vertebrate embryogenesis is a remarkably dynamic process during which numerous cell types of different lineages generate, change, or disappear within a short period of time. A major challenge in understanding this process is the lack of topographical transcriptomic information that can help correlate microenvironmental cues within the hierarchy of cell fate decisions. Here, we employed Stereo-seq, a high-definition spatially resolved transcriptomic technology, to dissect the spatiotemporal dynamics of gene expression and regulatory networks in the developing zebrafish embryos. We profiled 91 embryo sections covering six critical time points during the first 24 hours of development, obtaining a total of 139,391 spots at cellular size ([~]100 m2) with spatial coordinates. Meanwhile, we identified spatial modules and co-varying genes for specific tissue organizations. By performing the integrative analysis of the Stereo-seq and scRNA-seq data from each time point, we reconstructed the spatially resolved developmental trajectories of cell fate transitions and molecular changes during zebrafish embryogenesis. We further investigated the spatial distribution of ligand-receptor pairs for major signaling pathways and identified novel interactions that potentially crosstalk with the Notch signaling pathway during zebrafish development. Our study constitutes a fundamental reference for further studies aiming to understand vertebrate development.

developmental biology↗

bioRxiv2021

High-resolution spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae

Drosophila has long been a successful model organism in multiple fields such as genetics and developmental biology. Drosophila genome is relatively smaller and less redundant, yet largely conserved with mammals, making it a productive model in studies of embryogenesis, cell signaling, disease mechanisms, etc. Spatial gene expression pattern is critical for understanding of complex signaling pathways and cell-cell interactions, whereas temporal gene expression changes need to be tracked during highly dynamic activities such as tissue development and disease progression. Systematic studies in Drosophila as a whole are still impeded by lack of these spatiotemporal transcriptomic information. Drosophila embryos and tissues are of relatively small size, limiting the application of current technologies to comprehensively resolve their spatiotemporal gene expression patterns. Here, utilizing SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. Our data recapitulated the spatial transcriptomes of embryonic and larval development in Drosophila. With these data, we identified known and previously undetected subregions in several tissues during development, and revealed known and potential gene regulatory networks of transcription factors within their topographic background. We further demonstrated that Stereo-seq data can be used for 3D reconstruction of Drosophila embryo spatial transcriptomes. Our data provides Drosophila research community with useful resources of spatiotemporally resolved transcriptomic information across developmental stages.

developmental biology↗

bioRxiv2019

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

BackgroundIn spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.\n\nResultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.\n\nConclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

bioinformatics↗

bioRxiv2018

Removal of the C6 vaccinia virus interferon-β inhibitor in the hepatitis C vaccine candidate MVA-HCV elicited in mice high immunogenicity in spite of reduced host gene expression

Hepatitis C virus (HCV) represents a major global health problem for which a vaccine is not available. MVA-HCV is a unique HCV vaccine candidate based in the modified vaccinia virus Ankara (MVA) vector expressing the nearly full-length genome of HCV genotype 1a that elicits broad and polyfunctional CD8+ T-cell responses in mice. With the aim to improve the immune response of MVA-HCV and due to the importance of interferon (IFN) in HCV infection, we deleted in MVA-HCV the vaccinia virus (VACV) C6L gene, encoding an inhibitor of IFN-{beta} that prevents activation of the transcription factors IRF3 and IRF7. The resulting vaccine candidate (MVA-HCV {Delta}C6L) expresses all HCV antigens and deletion of C6L had no effect on viral growth in permissive chicken cells. In human monocyte-derived dendritic cells, infection with MVA-HCV {Delta}C6L triggered severe down-regulation of IFN-{beta}, IFN-{beta}-induced genes and cytokines similarly to MVA-HCV, as defined by real-time PCR and microarray analysis. In infected mice both vectors had a similar profile of recruited immune cells and induced comparable levels of adaptive and memory HCV-specific CD8+ T-cells, mainly against p7+NS2 and NS3 HCV proteins, with a T cell effector memory (TEM) phenotype. Furthermore, antibodies against E2 were also induced. Overall, our findings showed that while these vectors had a profound inhibitory effect on gene expression of the host, they strongly elicited CD8+ T cell and humoral responses against HCV antigens. These observations add support to the consideration of these vectors as potential vaccine candidates against HCV.\n\nIMPORTANCEHepatitis C virus represents a global health problem with 71 million of people infected worldwide. While direct-acting antivirals agents can cure hepatitis C virus infection in most of patients, their high cost and the emergence of drug resistant variants make them not a feasible and affordable strategy to eradicate the virus. Therefore, a vaccine is an urgent goal that requires efforts in understanding the correlates of protection for hepatitis C virus clearance. Poxvirus vectors, in particular the attenuated modified vaccinia virus Ankara, are ideal as vaccine candidates due to their ability to induce both T and B cell immune responses against heterologous antigens and protection against a wide spectrum of pathogens. Here we describe the generation, genetics and immunogenicity elicited by MVA-HCV {Delta}C6L, a novel vaccine candidate for hepatitis C virus that expresses nearly all of hepatitis C proteins but lacks an IFN-{beta} inhibitor, the C6 vaccinia virus protein.

immunology↗