Biology subjects

Alemany, A.

Publications and source records attributed to Alemany, A..

7 recordsLinked to original sources

bioRxiv2026

Sex-specific DNA methylation in adult skeletal muscle

DNA methylation is a key epigenetic mechanism influencing gene regulation and cellular identity. In skeletal muscle, methylation contributes to fiber-type specification, metabolic programming, and satellite cell function, with evidence of sex-specific differences. Here, we investigated whether spatial regionalization of gene expression along the proximal-distal axis of the tibialis anterior (TA) is mirrored by corresponding patterns of DNA methylation. Using MeDseq on TA sections from muscles previously analyzed by spatial transcriptomics, we profiled methylation across transcriptional start sites (TSS), gene bodies, and regulatory elements. Despite robust spatial differences in transcriptomes, methylation patterns were largely uniform along the proximal-distal axis, indicating that DNA methylation does not underlie regional gene expression in adult TA muscle. In contrast, sex emerged as the primary determinant of methylation variation. Male muscles exhibited widespread hypermethylation at TSS, gene-bodies and regulatory regions, corresponding with sex-specific transcriptional programs, including glycolytic fiber enrichment in males and oxidative fiber markers in females. Notably, chromatin- and methylation-associated regulators such as Setd7, Gsk3a, and Bmyc were upregulated in males, suggesting mechanisms linking transcriptional control to epigenetic state. These findings highlight that while spatial gene expression is transcriptionally driven, sex-specific epigenetic programs dominate adult skeletal muscle, underscoring the need to consider sex in multi-omic studies of muscle biology.

molecular biology↗

bioRxiv2025

De novo H3.3K27M-altered Diffuse Midline Glioma in human brainstem organoids to dissect GD2 CAR T cell function

Diffuse midline glioma (DMG) is a rare yet highly aggressive paediatric cancer primarily arising in the pontine region of the brainstem, necessitating the development of scalable patient-representative models for treatment advance1,2. Here, we developed an FGF4-driven human brainstem organoid model, with high representation of pontine glial lineages. By genetically engineering de novo H3.3K27M-altered DMG, we show that this brainstem glial specification is essential for driving DMG tumorigenesis, resulting in tumours that recapitulate the infiltrative nature and molecular heterogeneity of patient samples. By performing prolonged GD2 CAR T cell treatment in this model, we could mirror variable treatment outcomes as observed in the clinic3,4 and demonstrate a high level of CAR T cell transcriptional heterogeneity. From these CAR T cell functional states, we could identify the most potent effector population and validated NCAM1 as a selection marker for their enrichment. In contrast, NCAM1- cells were linked to a cellular stress response, previously associated to immunotherapy resistance5. Furthermore, incorporating the brain-resident myeloid compartment resulted in DMG-specific, largely immunosuppressive microglia subtypes6. These disease-representative microglia reduced GD2 CAR T cell treatment efficacy and we identified the functional profiles most susceptible to this microglia-dependent immune modulation. Thus, we present a scalable human DMG model with critical applications towards understanding CAR T cell functionality to aid therapy development for this detrimental disease.

cancer biology↗

bioRxiv2025

Cell type-specific eQTL detection from single-cell RNA-seq reveals post-transcriptional regulatory mechanisms in human islets

Gene regulatory networks (GRNs) must be robust to maintain cellular identity across individuals yet flexible to accommodate population genetic variants. Comparing expression quantitative trait loci (eQTLs) in health and disease can therefore reveal hidden players in gene regulation. Here, we developed a computational pipeline to infer post-transcriptional regulatory events and their functional consequences from eQTL signals located in the 3' untranslated region of genes using single-cell RNA sequencing. As a case study, we repurposed datasets from human islets of donors with and without type 2 diabetes (T2D). We identified eQTL landscapes differing by cell type and diabetes status, with cell-type specificity associated with gene differential expression and RNA-binding proteins. Integrating eQTLs with GWAS and miRNA hits linked variants in G6PC2, QDPR, and RELL1 to insulin secretion, endoplasmic reticulum stress, and oxidative phosphorylation. Our pipeline provides a framework to unravel post-transcriptional regulatory mechanisms in health and disease at cell type resolution.

genetics↗

bioRxiv2021

Droplet-based Single-cell Total RNA-seq Reveals Differential Non-Coding Expression and Splicing Patterns during Mouse Development

In recent years, single-cell transcriptome sequencing has revolutionized biology, allowing for the unbiased characterization of cellular subpopulations. However, most methods amplify the termini of polyadenylated transcripts capturing only a small fraction of the total cellular transcriptome. This precludes the detection of many long non-coding, short non-coding and non-polyadenylated protein-coding transcripts. Additionally, most workflows do not sequence the full transcript hindering the analysis of alternative splicing. We therefore developed VASA-seq to detect the total transcriptome in single cells. VASA-seq is compatible with both plate-based formats and droplet microfluidics. We applied VASA-seq to over 30,000 single cells in the developing mouse embryo during gastrulation and early organogenesis. The dynamics of the total single-cell transcriptome result in the discovery of novel cell type markers many based on non-coding RNA, an in vivo cell cycle analysis and an improved RNA velocity characterization. Moreover, it provides the first comprehensive analysis of alternative splicing during mammalian development.

molecular biology↗

bioRxiv2021

DUX4 induces a homogeneous sequence of molecular changes, culminating in the activation of a stem-cell-like transcriptional network and induction of apoptosis in somatic cells

Facioscapulohumeral muscular dystrophy (FSHD) is a muscle degenerative disease that disproportionally affects the muscles of the face, shoulder girdle and upper arms. FSHD is caused by the misexpression of Double Homeobox 4 (DUX4), a transcription factor that is normally expressed during early embryonic development. Ectopic expression of DUX4 in somatic cells is cytotoxic and leads to rapid apoptosis. To elucidate the mechanism by which DUX4 induces apoptosis, we determined the temporal transcriptional changes induced by Dux4 at the single-cell level. We observed that induction of DUX4 expression induces a non-random, consecutive sequence of transcriptional changes. DUX4 homogenously induces the activation of a stem cell signature and activates a network of transcription factors that is typically expressed during early embryogenesis and in pluripotent stem cells. Ultimately, these transcriptional changes trigger the induction of apoptosis, suggesting that the induction of this early stemness program is incompatible with a somatic cell program. Our findings shed new light on the timing and dynamic of DUX4-mediated transcriptional reprogramming and may help elucidate why the DUX4 stemness program is required during early embryogenesis, but incompatible with somatic cell viability.

molecular biology↗

bioRxiv2020

A genome-wide CRISPR/Cas phenotypic screen formodulators of DUX4 cytotoxicity reveals screen complications

Facioscapulohumeral muscular dystrophy (FHSD), a fundamentally complex muscle disorder that thus far remains untreatable. As the name implies, FSHD starts in the muscles of the face and shoulder gridle. The main perturbator of the disease is the pioneer transcription factor DUX4, which is misexpressed in affected tissues due to a failure in epigenetic repressive mechanisms. In pursuit of unraveling the underlying mechanism of FSHD and finding potential therapeutic targets or treatment options, we performed an exhaustive genome-wide CRISPR/Cas9 phenotypic rescue screen to identify modulators of DUX4 cytotoxicity. We found no key effectors other than DUX4 itself, suggesting treatment efforts in FSHD should be directed towards its direct modulation. The screen did however reveal some rare and unexpected Cas9-induced genomic events, that may provide important considerations for planning future CRISPR/Cas9 knock-out screens.

molecular biology↗

bioRxiv2020

Functional modules from variable genes: Leveraging percolation to analyze noisy, high-dimensional data

While measurement advances now allow extensive surveys of gene activity (large numbers of genes across many samples), interpretation of these data is often confounded by noise — expression counts can differ strongly across samples due to variation of both biological and experimental origin. Complimentary to perturbation approaches, we extract functionally related groups of genes by analyzing the standing variation within a sampled population. To distinguish biologically meaningful patterns from uninterpretable noise, we focus on correlated variation and develop a novel density-based clustering approach that takes advantage of a percolation transition generically arising in random, uncorrelated data. We apply our approach to two contrasting RNA sequencing data sets that sample individual variation — across single cells of fission yeast and whole animals of C. elegans worms — and demonstrate robust applicability and versatility in revealing correlated gene clusters of diverse biological origin, including cell cycle phase, development/reproduction, tissue-specific functions, and feeding history. Our technique exploits generic features of noisy high-dimensional data and is applicable, beyond gene expression, to feature-rich data that sample population-level variability in the presence of noise.Significance Statement Gene expression largely determines the fate of each cell and ultimately the development and behavior of the whole organism. Whereas most of our knowledge on gene regulatory networks has been obtained from perturbation experiments (e.g. manipulating environmental conditions, genotype, or other physiological variables), here we develop an alternative approach based on the analysis of naturally occurring variations across individuals within a population. Using both single-cell and whole-animal RNA sequencing data, we demonstrate how a rich set of co-regulated gene modules can be uncovered from transcriptomic variability of individuals within unperturbed populations. To robustly extract interpretable clusters from the strong noise background, we devise a novel, versatile clustering approach based on network theory. With a foundation in the generic behavior of random networks near their percolation critical point, our method is broadly applicable, beyond gene expression, to any noisy, high-dimensional data that sample variation across individuals within a population.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗