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Armingol, E.

Publications and source records attributed to Armingol, E..

14 recordsLinked to original sources

T cell receptor-ligand affinity quantitatively tunes transcriptome remodelling in vivo, inversely regulating cell division and interferon response

The strength of T cell receptor (TCR) engagement by antigenic ligands governs naive T cell activation, expansion and differentiation. However, the molecular changes underpinning this process in vivo remain incompletely understood. To address this, we coupled an influenza infection model of varied TCR-ligand affinity with high-dimensional protein and RNA measurements. As previously reported, high affinity stimulation drove greater expansion, with disproportionately abundant effector subsets. Experiments using a KLRG1-fate reporter showed that differentiation state biases affected cells from both direct and indirect memory differentiation trajectories. Early post-infection, these biases manifested in differential metabolic and proliferative activities. Examination of T cells showing signs of initial priming in vivo revealed that TCR-ligand affinity primarily changed the magnitude of TCR-induced transcriptome remodelling, not the genes involved. This quantitative tuning drove affinity-dependent amplification of ribosome biogenesis and suppression of interferon response genes before mitogenic diversion, and was associated with elevated TCR-induced transcription factor activity. Together, these data demonstrate the in vivo underpinnings of affinity-dependent responses and reveal how accumulation of TCR-induced signalling outputs translates binding properties into appropriate differentiation outcomes.

immunology↗

An integrated multimodal pan-organ atlas of the female reproductive system across the lifespan contextualises gynaecological pathologies

Single cell transcriptomics has transformed our knowledge of reproductive tissues, yet studies remain largely organ-specific and temporally limited, leaving an incomplete picture of how cell types are distributed across the reproductive system over a lifetime. Gynaecological conditions affect more than one in four females and frequently span multiple organs and life stages. To advance our understanding and treatment of these conditions, an integrated cellular reference is essential. Here we present the Human Female Reproductive System Cell Atlas v1: a single-cell transcriptomic resource integrating more than 2M cells across the ovary, fallopian tube, uterus, cervix and vagina over the lifespan and menstrual cycle, further integrated with spatial transcriptomics and chromatin accessibility profiling to define 210 cell types through community-based annotation. Cross-organ integration resolves shared and organ-specific cellular states, identifying uterine-specific perivascular populations lining uterine spiral arteries, hypoxia-sensing type 3 innate lymphoid cells (ILC3s) enriched in the uterus, and lipid-associated macrophages with distinct subsets in each reproductive organ, including a previously undescribed population shared between the uterus and fallopian tube. Cross-organ integration enables detection of ectopic epithelial populations in otherwise healthy donors, including endometrial-like cells within a paediatric ovary consistent with early endometriosis. Integration with genome-wide association studies (GWAS) reveals that risk variants for major gynecological conditions act in mesenchymal cell states defined by specific transcriptional programmes and spatial or temporal context - for instance, heavy menstrual bleeding risk is enriched in basal fibroblasts (SFRP5) of the regenerative endometrial compartment. An integrated chromatin accessibility atlas provides peak-to-gene maps across reproductive cell types, enabling nomination of disease effector genes and providing the first regulatory evidence linking a Polyendocrine Metabolic Ovarian Syndrome (PMOS) risk locus to INHBB in granulosa cells. Together, this resource establishes a cellular and molecular framework for reproductive biology and the pathogenesis of neglected gynaecological conditions.

Cell Biology↗

Spatial atlas of the ovary identifies molecular events in primordial follicle activation in humans

The human ovarian reserve is established prenatally, when granulosa cells encapsulate germ cells to form a pool of quiescent primordial follicles, that ultimately determines the female reproductive and endocrine lifespan. From birth to menopause, subsets of these quiescent follicles, located in the thin outer ovarian cortex, are activated and undergo a growth programme with progressive inward migration to the inner cortex, before either undergoing atresia or, during reproductive age, ovulation. Disruption of this process may lead to infertility, metabolic disorders and early menopause, yet early follicle development remains largely poorly understood. Here, we generate the most comprehensive single-cell and spatial multiomics atlas of the human pre- and postnatal ovarian cortex, integrating transcriptomic and chromatin profiles from over four million cells obtained from fetal and newly profiled pediatric and adult donors. We resolve the early granulosa cell trajectory at unprecedented resolution and identify a retinoic acid-associated regulatory switch accompanying follicle activation. We further show that stromal fibroblasts are not homogeneous, but instead form a dynamic scaffold establishing previously unrecognised morphogen and paracrine gradients that organise the cortex into functional niches supporting quiescent, growing, and atretic follicles. Finally, we identify ovarian lipid associated macrophages (oLAMs) that localise around follicles and are likely to support tissue remodelling during folliculogenesis. Together, this atlas provides a foundational blueprint for human ovarian development and homeostasis, and provides a framework for improving strategies in fertility preservation and in vitro follicle maturation.

cell biology↗

Human Genome-Scale Models of Metabolism and Gene Expression Reveal Resource Constraints of Cancer Cell Lines

Genome-scale metabolic models (M-models) provide mechanistic insight into intracellular metabolism by simulating fluxes subject to nutrient and energy resource constraints. However, they cannot account for a major component of resource allocation, since they do not explicitly account for the cost of producing and maintaining enzymes. Genome-scale models of metabolism and gene expression (ME-Models) address this by including gene expression reactions, but these have only been developed for prokaryotes due to the additional complexity and challenges of modeling eukaryotes. Here, we present the human ME-Model, which encodes transcription, translation, complex formation, and turnover reactions for all enzymes catalyzing metabolic reactions, and couples these processes to constrain metabolic fluxes. We introduce humanME, a Python package to build and analyze human ME-Models. With this, we constructed 16 cancer cell line ME-Models. We found that resource constraints improve growth-rate predictions, and that ME-Model flux predictions are more biologically plausible and efficient. Moreover, transcriptional fluxes recapitulate RNA-Seq expression levels, with discrepancies revealing potential trade-offs involving multiple cellular objectives. Finally, the ME-Model recapitulates the Warburg effect, with increasing growth rate inducing glycolytic shifts, in part due to machinery costs of the electron transport chain. Altogether, we show ME-modeling can mechanistically link gene expression, resource allocation, and metabolism in human cells, substantially expanding the predictive scope of constraint-based models.

systems biology↗

Atlas-scale metabolic activities inferred from single-cell and spatial transcriptomics

Metabolism supplies energy, building blocks, and signaling molecules vital for cell function and communication, but methods to directly measure it at single-cell and/or spatial resolutions remain technically challenging and inaccessible for most researchers. Single-cell and spatial transcriptomics offer high-throughput data alternatives with a rich ecosystem of computational tools. Here, we present scCellFie, a computational framework to infer metabolic activities from human and mouse transcriptomic data at single-cell and spatial resolution. Applied to [~]30 million cell profiles, we generated a comprehensive metabolic atlas across human organs, identifying organ- and cell-type-specific activities. In the endometrium, scCellFie reveals metabolic programs contributing to healthy tissue remodeling during the menstrual cycle, with temporal patterns replicated in data from in vitro cultures. We also uncover disease-associated metabolic alterations in endometriosis and endometrial carcinoma, linked to proinflammatory macrophages, and metabolite-mediated epithelial cell communication, respectively. Ultimately, scCellFie provides a scalable toolbox for extracting interpretable metabolic functionalities from transcriptomic data.

bioinformatics↗

A developmental cell atlas of the human thyroid gland

The primary function of the thyroid gland is the synthesis and release of thyroid hormones, which are essential for health from embryogenesis to adulthood. Thyroid disorders occur frequently and include congenital hypothyroidism, which occurs due to aberrant thyroid development (thyroid dysgenesis) or impaired hormone synthesis and is particularly prevalent in trisomy 21 (T21). In contrast, thyroid carcinoma, an acquired disorder, is the most common endocrine malignancy in both paediatric and adult populations. Understanding the molecular basis of thyroid dysgenesis and paediatric thyroid carcinoma remains challenging, and requires an improved understanding of foetal thyroid development. To address this, we generated a comprehensive spatiotemporal atlas of the human thyroid during the first and second trimesters of pregnancy. Profiling over 200,000 cells with single-cell sequencing revealed key cell types involved in thyroid gland development, including the hormone-producing thyrocytes. We discovered that foetal thyroid follicular cells are heterogeneous epithelial populations consisting of two main functional subtypes (fTFC1, fTFC2), with fTFC2 expressing increased levels of PAX8, and spatial transcriptomics revealed subtype co-occurrence within individual follicles. While both fTFC1 and fTFC2 persist in adult thyroid, fTFC2 is a minor population amongst additional PAX8-positive follicular cell subsets. We observed thyroid dysgenesis in T21 age-matched specimens, and T21 thyrocytes showed transcriptional signatures of cytoskeletal disorganisation and altered interactions with the extracellular matrix, as well as compensatory activation of metabolic stress gene programs and upregulation of thyroid biosynthetic genes. In line with the altered proportions of fTFC2 in healthy foetal and adult thyroid, papillary thyroid cancer in children is transcriptionally enriched for the fTFC2 signature compared to that in adults. All together, these findings reveal thyrocyte heterogeneity across the lifespan and provide insights into thyroid development in health and disease, informing potential therapeutic interventions.

genomics↗

Protein structure, a genetic encoding for glycosylation

Unlike DNA, RNA, and protein biosynthesis, dogma describes glycosylation as primarily determined by intrinsic cellular limitations, such as glycosyltransferase expression and precursor availability. However, this cannot explain the commonly-observed differences between glycans on the same protein. By examining site-specific glycosylation on diverse human proteins, we detected associations between protein structure and glycan structure, broadly generalizable to human-expressed glycoproteins. Through structural analysis of site-specific glycosylation data, we found protein-sequence and structural features consistently correlated with specific glycan features. To quantify these relationships, we present a new amino acid substitution matrix describing "glycoimpact", i.e., the association of primary protein structure and glycosylation. High-glycoimpact amino acids co-evolve with glycosites, and glycoimpact is high when estimates of amino acid conservation and variant pathogenicity diverge. We report thousands of disease variants near glycosites with high-glycoimpact, including several with known links to aberrant glycosylation (e.g., Oculocutaneous Albinism, Jakob-Creutzfeldt disease, Gerstmann-Straussler-Scheinker, and Gauchers Disease). Finally, glycoimpact quantification is validated by studying oligomannose-complex glycan ratios on HIV ENV, differential sialylation on IgG3 Fc, differential glycosylation on SARS-CoV-2 Spike, and fucose-modulated function of a tuberculosis monoclonal antibody. Finally, to test the causality of protein-glycan associations, we created 5 glycoimpact-designed novel Rituximab variants, 4 of which substantially changed glycoprofiles as predicted. In all, we report that site-specific glycan biosynthesis is influenced by underlying protein structure, enabling glycan structure prediction and genetic sequence-guided glycoengineering.

genetics↗

Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network

Glycosylation is described as a non-templated biosynthesis. Yet, the template-free premise is antithetical to the observation that different N-glycans are consistently placed at specific sites. It has been proposed that glycosite-proximal protein structures could constrain glycosylation and explain the observed microheterogeneity. Using site-specific glycosylation data, we trained a hybrid neural network to parse glycosites (recurrent neural network) and match them to feasible N-glycosylation events (graph neural network). From glycosite-flanking sequences, the algorithm predicts most human N-glycosylation events documented in the GlyConnect database and proposed structures corresponding to observed monosaccharide composition of the glycans at these sites. The algorithm also recapitulated glycosylation in Enhanced Aromatic Sequons, SARS-CoV-2 spike, and IgG3 variants, thus demonstrating the ability of the algorithm to predict both glycan structure and abundance. Thus, protein structure constrains glycosylation, and the neural network enables predictive in silico glycosylation of uncharacterized or novel protein sequences and genetic variants.

bioinformatics↗

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes [~]1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of [~]63k cells, 10 cell types, and 12 samples.

bioinformatics↗

Whole-body gene expression atlas of an adult metazoan

Animals are integrated organ systems composed of interacting cells whose structure and function are in turn defined by their active genes. Understanding what distinguishes physiological and disease states therefore requires systemic knowledge of the gene activities that define the distinct cells that make up an animal. Towards this goal, this study reports the first single-cell resolution transcriptional atlas of a fertile multicellular organism: Caenorhabditis elegans. The scRNA-Seq compendium of wild-type young adult C. elegans comprises 159 distinct cell types with 18,033 genes expressed across cell types. Fewer than 300 of these genes are housekeeping genes as evidenced by their consistent expression across cell types and conditions, and by their basic and essential functions; 170 of these housekeeping genes are conserved across phyla. The 362 transcription factors with available ChIP-Seq data are linked to patterns of gene expression of different cell types. To identify potential interactions between cell types, we used the in silico tool cell2cell to predict molecular patterns reflecting both known and uncharacterized intercellular interactions across the C. elegans body. Finally, we present WormSeq (wormseq.org), a web interface that, among other functions, enables users to query gene expression across cell types, identify cell-type specific and potential housekeeping genes, analyze candidate ligand-receptors mediating communication between cells, and study promiscuous and cell-specific transcription factors. The datasets, analyses, and tools presented here will enable the generation of testable hypotheses about the cell and organ-specific function of genes in diverse biological contexts.

genomics↗

Unraveling the coordinated dynamics of protein- and metabolite-mediated cell-cell communication

SummaryCell-cell communication dynamically changes across time while involving diverse cell populations and ligand types such as proteins and metabolites. While single-cell transcriptomics enables its inference, existing tools typically analyze ligand types separately and overlook their coordinated activity. Here, we present Tensor-cell2cell v2, a computational tool that can jointly analyze protein- and metabolite-mediated communication over time using coupled tensor component analysis, while preserving each modality of inferred communication scores independently, as well as their data structures and distributions. Applied to brain organoid development, Tensor-cell2cell v2 uncovers dynamic, coordinated communication programs involving key proteins and metabolites across relevant cell types across specific time points. Availability and implementationTensor-cell2cell v2 and its new coupled tensor component analysis are implemented in Python and available as part of the cell2cell framework at https://github.com/earmingol/cell2cell. This python library is available on PyPI. Analyses of this manuscript can be reproduced in a Code Ocean capsule at https://doi.org/10.24433/CO.0061424.v1 and online tutorials can be found at https://cell2cell.readthedocs.io. Supplementary informationSupplementary data are available at bioRxiv online.

bioinformatics↗

Context-aware deconvolution of cell-cell communication with Tensor-cell2cell

Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell-cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell-cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which is the first strategy to decipher context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell-cell and ligand receptor pairs) linked to COVID-19 severities and Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.

bioinformatics↗

Inferring the spatial code of cell-cell interactions and communication across a whole animal body

Cell-cell interactions shape cellular function and ultimately organismal phenotype. However, the code embedded in the molecular interactions driving and sustaining the spatial organization of cells remains to be elucidated. Here we present a computational framework to infer the spatial code underlying cell-cell interactions from the transcriptomes of the cell types across the whole body of a multicellular organism. As core of this framework, we introduce our tool cell2cell, which uses the coexpression of ligand-receptor pairs to compute the potential for intercellular interactions, and we test it across the Caenorhabditis elegans body. Leveraging a 3D atlas of C. elegans cells, we also implement a genetic algorithm to identify the ligand-receptor pairs most informative of the spatial organization of cells. Validating the spatial code extracted with this strategy, the resulting intercellular distances are negatively correlated with the inferred cell-cell interactions. Furthermore, for selected cell-cell and ligand-receptor pairs, we experimentally confirm the communicatory behavior inferred with cell2cell and the genetic algorithm. Thus, our framework helps identify a code that predicts the spatial organization of cells across a whole-animal body.

bioinformatics↗

Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity

The human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS). Studies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner. Here, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex. Common human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified. The prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls. The presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age. In vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells. HS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection. Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/238444v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@14ff1ecorg.highwire.dtl.DTLVardef@193d84corg.highwire.dtl.DTLVardef@15d6f9eorg.highwire.dtl.DTLVardef@14b16c6_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract. Diagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS).It is well known that host microbes groom the mucosa where they reside. Recent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection. In this study we examine the impact of microbial modification of HS on viral attachment. C_FIG

microbiology↗