Biology subjects

Talavera-Lopez, C.

Publications and source records attributed to Talavera-Lopez, C..

12 recordsLinked to original sources

bioRxiv2026

An early CYP26A1/CRYAA progenitor-glial domain marks the presumptive macular region during human retinal development

The human macula, a high-acuity retinal region essential for central vision, emerges early in development, but the cellular basis of its regional specialisation remains incompletely resolved. Building on recent studies that identified CYP26A1-mediated retinoic acid (RA) suppression in the presumptive macula, we define a CRYAA-positive progenitor-glial compartment in the temporal human retina from post-conception week (pcw) 7 onward. Single-cell RNA sequencing, immunohistochemistry and spatial morphometry indicate that this compartment is linked to regional gliogenic maturation and later corresponds to a specialised macular Muller glial population, rather than simply reflecting uniform pan-retinal developmental timing. Spatial mapping shows that the CYP26A1-positive domain remains delimited as the surrounding retina expands. Adult tissue analysis shows that CYP26A1 expression is retained in primate macular Muller glia and in corresponding acute-zone regions of visual-streak species. These findings extend current models of CYP26A1-mediated RA modulation in human acute zone/macular development by linking the early CYP26A1 domain to a regionally specialised CRYAA-positive progenitor-glial compartment. We propose that early macular patterning includes a spatially restricted glial programme that may contribute to later regional architecture and disease vulnerability.

developmental biology↗

bioRxiv2024

Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning

Spatial omics allow us to identify and analyze communities of cells coordinating specific functions within a tissue. While these communities, defined as cell niches, are fundamentally shaped by interactions between spatially neighboring cells, we lack computational frameworks that can leverage spatial omics data to quantitatively characterize niches based on cell interaction events. To address this, we introduce NicheCompass, a graph deep learning method designed based on the principles of cellular communication. NicheCompass not only identifies cell niches, but also learns and informs about the signaling events shaping the identity of these niches. Unlike existing methods, it uniquely characterizes niches by quantifying their activity of spatial gene programs which represent diverse mechanisms of cell-cell communication and transcriptional regulation, thereby uncovering the underlying cellular processes constituting each niche. We showcase a comprehensive workflow encompassing data integration, niche identification, and functional interpretation, and demonstrate that, with its biologically informed design, NicheCompass outperforms existing methods. NicheCompass is broadly applicable to spatial transcriptomics data, which we illustrate by mapping the architecture of diverse tissues during mouse embryonic development, and delineating basal (KRT14) and luminal (KRT8) tumor niches in human breast cancer. We further introduce fine-tuning-based spatial reference mapping, revealing an SPP1+ macrophage-dominated tumor niche in non-small cell lung cancer patients. Additionally, we extend NicheCompass to multimodal spatial profiling of gene expression and chromatin accessibility, identifying and characterizing distinct white matter niches in the mouse brain. Finally, we apply NicheCompass to a whole mouse brain spatial atlas with 8.4 million cells demonstrating its scalability and ability to build foundational, interpretable spatial representations for entire organs. Overall, NicheCompass provides a novel approach to the challenge of identifying and analyzing niches, and suggests a more rigorous niche definition grounded in the quantitative characterization of underlying cellular processes.

bioinformatics↗

bioRxiv2023

Spatially resolved multiomics of human cardiac niches

A cells function is defined by its intrinsic characteristics and its niche: the tissue microenvironment in which it dwells. Here, we combine single-cell and spatial transcriptomic data to discover cellular niches within eight regions of the human heart. We map cells to micro-anatomic locations and integrate knowledge-based and unsupervised structural annotations. For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors, and cell interactions using a custom CellPhoneDB.org module. We show that the sinoatrial node is compartmentalised, with a core of pacemaker cells, fibroblasts and glial cells supporting paracrine glutamatergic signalling. We introduce a druggable target prediction tool, drug2cell, which leverages single-cell profiles and drug-target interactions, providing unexpected mechanistic insights into the chronotropic effects of drugs, including GLP-1 analogues. In the epicardium, we show enrichment of both IgG+ and IgA+ plasma cells forming immune niches which may contribute to infection defence. We define a ventricular myocardial-stress niche enriched for activated fibroblasts and stressed cardiomyocytes, cell states that are expanded in cardiomyopathies. Overall, we provide new clarity to cardiac electro-anatomy and immunology, and our suite of computational approaches can be deployed to other tissues and organs.

genomics↗

bioRxiv2022

A comparative 'omics' approach for prediction of candidate Strongyloides stercoralis diagnostic coproantigens

Human infection with the intestinal nematode Strongyloides stercoralis is persistent unless effectively treated, and potentially fatal in immunosuppressed individuals. Epidemiological data are lacking due to inadequate diagnosis. A rapid antigen detection test is a priority for population surveillance, validating cure after treatment, and for screening prior to immunosuppression. We analysed open access omics data sets and used online predictors to identify S. stercoralis proteins that are likely to be present in infected stool, Strongyloides-specific, and antigenic. Transcriptomic data from gut and non-gut dwelling life cycle stages of S. stercoralis revealed 328 proteins that are differentially expressed. Strongyloides ratti proteomic data for excreted and secreted (E/S) proteins were matched to S. stercoralis, giving 1,057 orthologues. Five parasitism-associated protein families (SCP/TAPS, prolyl oligopeptidase, transthyretin-like, aspartic peptidase, acetylcholinesterase) were compared phylogenetically between S. stercoralis and outgroups, and proteins with least homology to the outgroups were selected. Proteins that overlapped between the transcriptomic and proteomic datasets were analysed by multiple sequence alignment, epitope prediction and 3D structure modelling to reveal S. stercoralis candidate peptide/protein coproantigens. We describe 22 candidates from seven genes, across all five protein families for further investigation as potential S. stercoralis diagnostic coproantigens, identified using open access data and freely-available protein analysis tools. This powerful approach can be applied to many parasitic infections with omic data to accelerate development of specific diagnostic assays for laboratory or point-of-care field application. Author summaryThe worm Strongyloides stercoralis causes infectious disease in people throughout tropical and sub-tropical regions, leading to an extensive reduction in quality of life and even death. Millions of people are at risk of infection with this parasite and improved diagnostic and control methods and technologies are urgently required. Currently, most diagnosis is carried out through methods involving visual inspection of patients faeces, which has a number of drawbacks, particularly its poor sensitivity. This paper presents a new method to develop improved diagnostic tests for S. stercoralis, by computational analysis of publicly available gene and protein sequences to predict proteins that may be detectable in faeces. This would enable the development of rapid diagnostic tests in the form of lateral flows or dipsticks, with better predictive ability and fewer drawbacks than current diagnostic methods. A number of potential proteins, predicted to have all the desired characteristics for use in such tests were found through the new method and have been presented in this paper. With validation, new diagnostic tests for S. stercoralis could be developed from these results and the computational approach could be used to target other parasitic diseases.

bioinformatics↗

bioRxiv2022

An integrated cell atlas of the human lung in health and disease

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

cell biology↗

bioRxiv2022

Biologically informed deep learning to infer gene program activity in single cells

The increasing availability of large-scale single-cell datasets has enabled the detailed description of cell states across multiple biological conditions and perturbations. In parallel, recent advances in unsupervised machine learning, particularly in transfer learning, have enabled fast and scalable mapping of these new single-cell datasets onto reference atlases. The resulting large-scale machine learning models however often have millions of parameters, rendering interpretation of the newly mapped datasets challenging. Here, we propose expiMap, a deep learning model that enables interpretable reference mapping using biologically understandable entities, such as curated sets of genes and gene programs. The key concept is the substitution of the uninterpretable nodes in an autoencoders bottleneck by labeled nodes mapping to interpretable lists of genes, such as gene ontologies, biological pathways, or curated gene sets, for which activities are learned as constraints during reconstruction. This is enabled by the incorporation of predefined gene programs into the reference model, and at the same time allowing the model to learn de novo new programs and refine existing programs during reference mapping. We show that the model retains similar integration performance as existing methods while providing a biologically interpretable framework for understanding cellular behavior. We demonstrate the capabilities of expiMap by applying it to 15 datasets encompassing five different tissues and species. The interpretable nature of the mapping revealed unreported associations between interferon signaling via the RIG-I/MDA5 and GPCRs pathways, with differential behavior in CD8+ T cells and CD14+ monocytes in severe COVID-19, as well as the role of annexins in the cellular communications between lymphoid and myeloid compartments for explaining patient response to the applied drugs. Finally, expiMap enabled the direct comparison of a diverse set of pancreatic beta cells from multiple studies where we observed a strong, previously unreported correlation between the unfolded protein response and asparagine N-linked glycosylation. Altogether, expiMap enables the interpretable mapping of single cell transcriptome data sets across cohorts, disease states and other perturbations.

bioinformatics↗

bioRxiv2021

Experimental microevolution of Trypanosoma cruzi reveals hybridization and clonal mechanisms driving rapid diversification of genome sequence and structure.

Protozoa and fungi are known to have extraordinarily diverse mechanisms of genetic exchange. However, the presence and epidemiological relevance of genetic exchange in Trypanosoma cruzi, the agent of Chagas disease, has been controversial and debated for many years. Field studies have identified both predominantly clonal and sexually recombining natural populations. Two of six natural T. cruzi lineages (TcV and TcVI) show hybrid mosaicism, using analysis of single-gene locus markers. The formation of hybrid strains in vitro has been achieved and this provides a framework to study the mechanisms and adaptive significance of genetic exchange. Using whole genome sequencing of a set of experimental hybrids strains, we have confirmed that hybrid formation initially results in tetraploid parasites. The hybrid progeny showed novel mutations that were not attributable to either (diploid) parent showing an increase in amino acid changes. In long-term culture, up to 800 generations, there was progressive, gradual erosion of progeny genomes towards triploidy, yet retention of elevated copy number was observed at several core housekeeping loci. Our findings indicate hybrid formation by fusion of diploid T. cruzi, followed by sporadic genome erosion, but with substantial potential for adaptive evolution, as has been described as a genetic feature of other organisms, such as some fungi.

microbiology↗

bioRxiv2021

scvi-tools: a library for deep probabilistic analysis of single-cell omics data

AO_SCPLOWBSTRACTC_SCPLOWProbabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

bioinformatics↗

bioRxiv2020

Activation of a neural stem cell transcriptional program in parenchymal astrocytes

Neural stem cells, located in discrete niches in the adult brain, generate new neurons throughout life. These stem cells are specialized astrocytes, but astrocytes in other brain regions do not generate neurons under physiological conditions. After stroke, however, striatal astrocytes undergo neurogenesis in mice, triggered by decreased Notch signaling. We used single-cell RNA sequencing to characterize neurogenesis by Notch-depleted striatal astrocytes in vivo. Striatal astrocytes were located upstream of neural stem cells in the neuronal lineage. As astrocytes initiated neurogenesis, they became transcriptionally very similar to subventricular zone stem cells and progressed through a nearly identical neurogenic program. Surprisingly, in the non- neurogenic cortex, Notch-depleted astrocytes also initiated neurogenesis. Yet, the cortical astrocytes, and many striatal ones, stalled before entering transit- amplifying divisions. Infusion of epidermal growth factor enabled stalled striatal astrocytes to resume neurogenesis. We conclude that parenchymal astrocytes are latent neural stem cells and that targeted interventions can guide them through their neuronal differentiation.

neuroscience↗

bioRxiv2020

Cells and gene expression programs in the adult human heart

Cardiovascular disease is the leading cause of death worldwide. Advanced insights into disease mechanisms and strategies to improve therapeutic opportunities require deeper understanding of the molecular processes of the normal heart. Knowledge of the full repertoire of cardiac cells and their gene expression profiles is a fundamental first step in this endeavor. Here, using large-scale single cell and nuclei transcriptomic profiling together with state-of-the-art analytical techniques, we characterise the adult human heart cellular landscape covering six anatomical cardiac regions (left and right atria and ventricles, apex and interventricular septum). Our results highlight the cellular heterogeneity of cardiomyocytes, pericytes and fibroblasts, revealing distinct subsets in the atria and ventricles indicative of diverse developmental origins and specialized properties. Further we define the complexity of the cardiac vascular network which includes clusters of arterial, capillary, venous, lymphatic endothelial cells and an atrial-enriched population. By comparing cardiac cells to skeletal muscle and kidney, we identify cardiac tissue resident macrophage subsets with transcriptional signatures indicative of both inflammatory and reparative phenotypes. Further, inference of cell-cell interactions highlight a macrophage-fibroblast-cardiomyocyte network that differs between atria and ventricles, and compared to skeletal muscle. We expect this reference human cardiac cell atlas to advance mechanistic studies of heart homeostasis and disease.

genomics↗

bioRxiv2019

Clonally distinct differentiation trajectories shape CD8+ memory T cell heterogeneity after acute viral infections in humans

CD8+ T cells play essential roles in immunity to viral and bacterial infections, and to guard against malignant cells. The CD8+ T cell response to an antigen is composed of many T cell clones with unique T cell receptors, together forming a heterogenous repertoire of phenotypically and functionally distinct effector and memory cells1, 2. How individual T cell clones contribute to this heterogeneity during an immune response is key to understand immunity but remains largely unknown. Here, we longitudinally tracked CD8+ T cell clones expanding in response to yellow fever virus vaccination at the single cell level in humans. We show that only a fraction of the clones detected in the acute response persists as circulating memory T cells, indicative of clonal selection. Clones persisting in the memory phase displayed biased differentiation trajectories along a gradient of stem cell memory (SCM) towards terminally differentiated effector memory (EMRA) fates. Reactivation of single memory CD8+ T cells revealed that they were poised to recapitulate skewed differentiation trajectories in secondary responses, and this was generalizable across individuals for both yellow fever and influenza virus. Together, we show that the sum of distinct clonal differentiation repertoires results in the multifaceted T cell response to acute viral infections in humans.

immunology↗

bioRxiv2018

Repeat-driven generation of antigenic diversity in a major human pathogen, Trypanosoma cruzi

Trypanosoma cruzi, a zoonotic kinetoplastid protozoan with a complex genome, is the causative agent of American trypanosomiasis (Chagas disease). The parasite uses a highly diverse repertoire of surface molecules, with roles in cell invasion, immune evasion and pathogenesis. Thus far, the genomic regions containing these genes have been impossible to resolve and it has been impossible to study the structure and function of the several thousand repetitive genes encoding the surface molecules of the parasite. We here present an improved genome assembly of a T. cruzi clade I (TcI) strain using high coverage PacBio single molecule sequencing, together with Illumina sequencing of 34 T. cruzi TcI isolates and clones from different geographic locations, sample sources and clinical outcomes. Resolution of the surface molecule gene structure reveals an unusual duality in the organisation of the parasite genome, a core genomic region syntenous with related protozoa flanked by unique and highly plastic subtelomeric regions encoding surface antigens. The presence of abundant interspersed retrotransposons in the subtelomeres suggests that these elements are involved in a recombination mechanism for the generation of antigenic variation and evasion of the host immune response. The comparative genomic analysis of the cohort of TcI strains revealed multiple cases of such recombination events involving surface molecule genes and has provided new insights into T. cruzi population structure.

genomics↗