Biology subjects

Mahfouz, A.

Publications and source records attributed to Mahfouz, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Spatial transcriptomics maps region and isoform specific brain alterations in mdx and mdx4cv models of Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is accompanied by cognitive and neurodevelopmental difficulties. To investigate how the loss of different dystrophin isoforms affects gene expression across brain regions, we performed spatial transcriptomics on brains from mdx and mdx4cv mouse models (lacking Dp427 or Dp427+Dp140 respectively) and healthy controls aged 2 weeks. Both dystrophic models showed transcriptional changes converging on myelin and oligodendrocyte biology and oxidative phosphorylation, but with distinct regional patterns. In mdx mice, myelin genes were upregulated in the amygdala and hippocampus and downregulated in fiber tract regions, while mitochondrial genes were upregulated in most regions but downregulated in the thalamus and isocortex. In mdx4cv mice, myelin genes were broadly upregulated and mitochondrial genes downregulated across almost all regions. Direct comparison between mdx and mdx4cv mice demonstrated that the additional loss of Dp140 expanded localized myelin upregulation across the brain and shifts the mitochondrial response toward widespread downregulation. These differences in regional changes between the mouse models is in line with their dystrophin isoform(s) loss. Our findings provide a molecular basis for the brain-related impairments of DMD and a foundation for future therapeutic studies.

neuroscience↗

bioRxiv2026

Zero-Shot Metabolite Prediction from Gene Expression via Physics-Informed Graph Neural Networks

Predicting metabolite concentrations from gene expression is instrumental for linking regulatory programs to metabolic phenotypes. Prior approaches rely on static enzyme-metabolite mappings and often omit data-driven learning or biochemical constraints, limiting their ability to generalize to new metabolites. We present GAZE (Graph Attention for Zero-shot metabolite Estimation), a physics-informed graph neural network that integrates enzyme expression, Enzyme Commission functional embeddings, and ChemBERTa metabolite descriptors within a unified metabolic graph (5,414 nodes, 16,307 edges). A Metabolite-Conditioned Reader uses each metabolites SMILES embedding to query learned pathway representations, enabling zero-shot prediction with no metabolite-specific parameters. We evaluate GAZE in three scenarios: (i) standard cross-validation on the Cancer Atlas of Metabolic Profiles (18,044 genes, 180 metabolites, 867 cell lines), achieving R2 = 0.816; (ii) leave-one-metabolite-out (LOMO) zero-shot evaluation across 50 held-out metabolites, where the physics-informed variant halves the median R2 deficit relative to a standard GNN baseline (-0.34 vs. -0.72), with 30% of unseen metabolites achieving positive R2; and (iii) external validation on an independent clear cell renal cell carcinoma tissue cohort (220 samples), where GAZE achieves median Spearman{rho} = 0.330 across 214 metabolites without fine-tuning. GAZE outperforms scCellFie, MEBOCOST, and UnitedMet across all evaluation settings.

bioinformatics↗

bioRxiv2025

Image-guided alignment of consecutive multi-modal tissue slides

Multi-modal spatial data analysis often requires precise physical alignment of consecutive tissue sections, a process that can be challenging and typically relies on shared molecular markers or image recognition techniques. Here, we introduce COAST (Consecutive multi-Omics Alignment of Spatial Tissues), a method to reliably physically align consecutive tissue sections to produce a unified multi-modal molecular dataset suitable for downstream applications. COAST relies exclusively on the images associated with spatial data, eliminating the need for common molecular features or prior annotations. We demonstrate the effectiveness of COAST using spatial transcriptomics slides, where it achieves performance comparable to established uni-modal alignment tools. Applying COAST to spatial transcriptomics and metabolomics/lipidomics tissue sections from a mouse model of ischemia reperfusion injury allowed the investigation of lipid/metabolite features of transcriptionally-defined cell types. Overall, COAST offers a streamlined and integrative solution for multi-modal spatial data alignment.

bioinformatics↗

bioRxiv2025

Evaluating exon-skipping therapies targeting the central nervous system in Duchenne muscular dystrophy using high-resolution spatial transcriptomics

Duchenne muscular dystrophy (DMD) is marked by progressive muscle degeneration due to dystrophin deficiency. Despite cognitive impairments in up to one-third of patients, central nervous system (CNS)-targeted dystrophin restoration remains relatively underexplored compared to muscle-focused therapies. Since dystrophin is expressed in the brain during development and postnatally, it is important to assess which aspects of CNS pathology could be rescued. However, studying Dmd is difficult due to its size, low-abundance transcripts, and with multiple isoforms, limiting the sensitivity and isoform resolution of standard sequencing methods. To overcome these limitations, we applied Xenium spatial transcriptomics to target splice junctions, enabling isoform-specific and exon 51 skipping events detection across brain regions and cell types. Using this approach, we analyzed mdx52 mice lacking the full-length and Dp140 isoforms, treated with two exon 51 skipping therapies (antisense oligonucleotides or AAV-U7ex51). We observed distinct spatial expression patterns in the wild type brain between the full-length isoforms (Dp427c/m/p1) and shorter isoforms (Dp71 and Dp40). The full-length isoforms were predominantly expressed in cortex layers 2/3-6b and the CA1 region, while the shorter isoforms were localized to cortex layer 1 and the dentate gyrus. Among the exon skipping therapies tested, U7ex51 delivered neonatally induced broad exon skipping in neurons and effectively restored full-length isoforms in the targeted cells. This study introduces the first subcellular-resolution spatial transcriptomic atlas of dystrophin presence and rescue in the CNS and demonstrates a framework for evaluating gene therapies in spatially and transcriptionally complex tissues such as the brain.

neuroscience↗

bioRxiv2025

Mass cytometry data integration methods reveal rural-urban gradient of immune profiles across geography

The human immune system strongly varies across populations and is shaped by a wide range of host and environmental factors. As such, a rural compared to urban lifestyle has previously been associated with baseline differences in immune profiles, which can impact outcome of vaccination. The question whether there is a immune signature that associates with rural living irrespective of ancestry or geography has been addressed here along with which analytical methods are most suitable. Using three mass cytometry datasets, we studied shared and population-specific immune characteristics of healthy rural- or urban-living Indonesian, Senegalese and Tanzanian adults and urban-living Europeans. After harmonized preprocessing and quality control, 75.4 million cells were integrated using four different data-integration methods. Among these, CytoNorm with in silico references performed best, revealing shared differences in the differentiation state within the lymphocyte compartment that distinguishes rural from urban-living individuals. Differentiated CD4+ T cells expressing CTLA-4, PD-1 and ICOS characterized rural living, as did CD4+ T cells with high CD161 expression in combination with CRTH2 and GATA3. In the innate compartment, CD56- CD16 NK cells with low CD38 and CD11c expression were expanded in rural living subjects. Together, our results demonstrate that, using a harmonized analytical pipeline and machine learning-based integration, large-scale mass cytometry data can be combined across diverse cohorts to identify shared immunological hallmarks of rural living. These signatures may provide targets for modulating immune responses in future vaccine studies.

immunology↗

bioRxiv2025

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose. However, the reliability of current benchmarks is undermined by their narrow focus on Visium and brain tissue datasets, as well as incorrect interpretation of manual annotation as ground truth. Here, we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration, and metric evaluation, and is designed to rapidly incorporate new methods and datasets. SACCELERATOR currently includes 22 SAC methods applied to 15 datasets spanning 9 technologies and diverse tissue types. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods across tissues and platforms. We also demonstrate that anatomical labels commonly used as ground truths are often biased, potentially error-prone, and, in some cases, unsuitable for benchmarking efforts. Rather than scoring and comparing methods, we propose a consensus-guided workflow that aggregates clustering results to generate consensus representations. Descriptive spatial metrics highlight areas of high entropy where method disagreement is highest, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual methods and manual annotations. Our results underscore the need for iterative, expert-in-the-loop analysis and reveal that traditional evaluation metrics do not always capture the subjective qualities of results. By improving tissue annotation and addressing key benchmarking limitations, SACCELERATOR provides a robust foundation for advancing spatial omics research.

genomics↗

bioRxiv2025

FOXP1 differentially regulates the development of murine vasopressin and oxytocin magnocellular neurons

The neuropeptides arginine vasopressin (AVP) and oxytocin (OXT) are closely related. As neurohormones, AVP and OXT are mainly produced in magnocellular neurons (MCNs) located in the hypothalamus. Development of both neuron types requires coordinated expression of transcription factors OTP, SIM1, ARNT2 and POU3F2. However, the exact transcription factors involved in the diferential diferentiation of the AVP and OXT lineages are yet unknown. We used a publicly available single-cell RNA-sequencing dataset of the developing mouse hypothalamus to identify gene regulatory networks linked to AVP and OXT neuronal diferentiation. We identified RORA, EBF3, FOXP1, FOXP2, and BCL11B as transcription factors with possible relevance for Avp and Oxt MCN divergence. We then modeled developmental gene expression dynamics using computational cell fate mapping. This revealed enrichment of EBF3 and BCL11B in the Avp lineage, while FOXP1 and FOXP2 are enriched in the Oxt lineage. Next, in silico analysis of Avp and Oxt promoters found predicted binding sites for FOXP1 and FOXP2 in the Oxt promoter, suggesting a role in Oxt MCN diferentiation. Finally, we validated the role of one candidate (FOXP1) with a heterozygous knockout mouse line. Compared to wild-type littermates, we find decreased AVP and OXT neuron abundance, with OXT neurons disproportionally afected.

neuroscience↗

bioRxiv2025

Spatial metabolomics reveals persistent localized niche-specific metabolic failure in kidneys following ischemia-reperfusion injury

After acute kidney injury (AKI), the persistence of failed repair proximal tubule (FR-PT) cells is postulated to hamper kidney regeneration and increase the risk of chronic kidney disease. This fibrotic shift likely depends on microenvironmental interactions, which remain largely unstudied. To investigate this, we mapped the spatial metabolic architecture of post-ischemic kidneys using an untargeted semi-quantitative spatial metabolomics (qMSI) approach, integrated with high-resolution spatial transcriptomics. Unsupervised neighborhood clustering of qMSI data revealed distinct microenvironments. Lipidome profiles identified diffusely spread areas with persistent injury markers surrounding FR-PT cells. These niches exhibited decreased linoleic acid and elevated succinic acid levels, even in epithelial cells that appeared otherwise healthy. Corresponding transcriptomic profiles confirmed downregulation of oxidative phosphorylation and fatty acid {beta}-oxidation in these regions. Together, these findings point towards niche-specific metabolic failure and persistent mitochondrial dysfunction in areas considered healthy, underscoring the need to prioritize metabolic resuscitation to prevent long-term consequences of AKI.

biochemistry↗

bioRxiv2025

scAGG: Sample-level embedding and classification of Alzheimer's disease from single-nucleus data

Identifying key cell types and genes in Alzheimers Disease (AD) is crucial for understanding its pathogenesis and discovering therapeutic targets. Single-cell RNA sequencing technology (scRNAseq) has provided unprecedented opportunities to study the molecular mechanisms that underlie AD at the cellular level. In this study, we address the problem of sample-level classification of AD using scRNAseq data, where we predict the disease status of entire samples from the gene expression profiles of their cells, which are not necessarily all affected by the disease. We introduce scAGG (single-cell AGGregation), a sample-level classification model that uses a sample-level pooling mechanism to aggregate single-cell embeddings, and show that it can accurately classify AD individuals and healthy controls. We then investigate the latent space learnt by the model and find that the model learns an ordering of the cells corresponding to disease severity. Genes associated with this ordering are enriched in AD-linked pathways, including cytokine signalling, apoptosis, and metal ion response. We also evaluate two attention-based models that perform on par with scAGG, but entropy analysis of their attention scores reveals limited interpretability value. As scRNAseq is increasingly applied to large cohorts and cell-level disease association annotations do not exist, our approach provides a way to classify phenotypes from single-cell measurements. The yielded cell- and sample-level severity scores may enable identification of AD-associated cell subtypes, paving the way for targeted drug development and personalized treatment strategies in AD. Code is available at: http://github.com/timoverlaan/scAGG

bioinformatics↗

bioRxiv2024

gsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability

To investigate the functional significance of genetic risk loci identified through genome-wide association studies (GWASs), genetic loci are linked to genes based on their capacity to account for variation in gene expression, resulting in expression quantitative trait loci (eQTL). Following this, gene set analyses are commonly used to gain insights into functionality. However, the efficacy of this approach is hampered by small effect sizes and the burden of multiple testing. We propose an alternative approach: instead of examining the cumulative associations of individual genes within a gene set, we consider the collective variation of the entire gene set. We introduce the concept of gene set QTL (gsQTL), and show it to be more adept at identifying links between genetic risk variants and specific gene sets. Notably, gsQTL experiences less susceptibility to inflation or deflation of significant enrichments compared with conventional methods. Furthermore, we demonstrate the broader applicability of shared variability within gene sets. This is evident in scenarios such as the coordinated regulation of genes by a transcription factor or coordinated differential expression.

bioinformatics↗

bioRxiv2024

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

Alternative splicing contributes to molecular diversity across brain cell types. RNA-binding proteins (RBPs) regulate splicing, but the genome-wide mechanisms remain poorly understood. Here, we used RBP binding sites and/or the genomic sequence to predict exon inclusion in neurons and glia as measured by long-read single-cell data in human hippocampus and frontal cortex. We found that alternative splicing is harder to predict in neurons compared to glia in both brain regions. Comparing neurons and glia, the position of RBP binding sites in alternatively spliced exons in neurons differ more from non-variable exons indicating distinct splicing mechanisms. Model interpretation pinpointed RBPs, including QKI, potentially regulating alternative splicing between neurons and glia. Finally, using our models, we accurately predict and prioritize the effect of splicing QTLs. Taken together, our models provide new insights into the mechanisms regulating cell-type-specific alternative splicing and can accurately predict the effect of genetic variants on splicing.

bioinformatics↗

bioRxiv2023

Integrative learning of disentangled representations from single-cell RNA-sequencing datasets

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA-sequencing is instrumental in studying cellular diversity in biological systems. Using batch correction methods, cell identities are often jointly defined across multiple conditions, individuals, or modalities. These approaches overlook group-specific information and require either paired data or matching features across datasets. Here we present shared-private Variational Inference via Product of Experts with Supervision (spVIPES), a framework to analyze the shared and private components of unpaired groups of cells with non-matching features. spVIPES represents the cells from the different groups as a composite of private and shared factors of variation using a probabilistic latent variable model. We evaluate the performance of spVIPES with a simulated dataset and apply our model in three different scenarios: (i) cross-species comparisons, (ii) regeneration following long and short acute kidney injury, and (iii) IFN-{beta} stimulation of PMBCs. In our study, we demonstrate that spVIPES accurately disentangles distinct sources of variation into private and shared representations while matching current state-of-the-art methods for batch correction. Furthermore, spVIPES shared space outperforms alternatives models at learning cell identities across datasets with non-matching features. We implemented spVIPES using the scvi-tools framework and release it as an open-source software at https://github.com/nrclaudio/spVIPES.

bioinformatics↗

bioRxiv2023

Integration of Mass Cytometry and Mass Spectrometry Imaging for Spatially Resolved Single Cell Metabolic Profiling

Integration of spatial omics technologies can provide important insights into the biology of tissues. We combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on the same single tissue section to reveal metabolic heterogeneity within tissues and its association with specific cell populations like cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components.

cancer biology↗

bioRxiv2023

An integrated single-cell RNA-seq atlas of the mouse hypothalamic paraventricular nucleus links transcriptional and functional types

The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic regulation through neuroendocrine signalling, outflow of the autonomic nervous system, and projections to other brain areas. The past years, single-cell datasets of the hypothalamus have contributed immensely to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been adequately classified functionally, its molecular classification is currently still insufficient. To address this, we created a detailed atlas of PVN transcriptional cell types by integrating various PVN single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we functionally profiled transcriptional cell types, based on relevant literature, existing retrograde tracing data and existing single-cell data of a PVN-projection target region. In our PVN atlas dataset, we identify the well-known different neuropeptide types, each composed of multiple novel subtypes. We identify Avp-Tac1, Avp-Th, Oxt-Foxp1, Crh-Nr3c1 and Trh-Nfib as the most important neuroendocrine subtypes based on markers described in literature. To characterize the pre-autonomic functional population, we integrated a single-cell retrograde tracing study of spinally-projecting pre-autonomic neurons into our PVN atlas. We identify these (pre-sympathetic) neurons to co-cluster with the Adarb2 + clusters in our dataset. Finally, we identify expression of receptors for Crh, Oxt, Penk, Sst, and Trh in the dorsal motor nucleus of the vagus, a key region that pre-parasympathetic PVN neurons project to. Concluding, our study present a detailed overview of the transcriptional cell types of the murine PVN, and provides a first attempt to resolve functionality for the identified populations.

neuroscience↗

bioRxiv2023

Benchmarking Variational AutoEncoders on cancer transcriptomics data

Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are difficult to train due to the large number of hyperparameters that need to be tuned. To get a better understanding of the importance of the different hyperparameters, we examined six different VAE models when trained on TCGA transcriptomics data and evaluated on the downstream task of cluster agreement with cancer subtypes. We studied the effect of the latent space dimensionality, learning rate, optimizer and initialization on the quality of subsequent clustering of the TCGA samples. We found {beta}-TCVAE and DIP-VAE to have a good performance, on average, despite being more sensitive to hyperparameters selection. Based on these experiments, we derived recommendations for selecting the different hyperparameters settings. In addition, we examined whether the learned latent spaces capture biologically relevant information. Hereto, we correlated the different representations with various data characteristics such as age, days to metastasis, immune infiltration, and mutation signatures. We found that for all models the latent factors, in general, do not uniquely correlate with one of the data characteristics even for models specifically designed for disentanglement.

bioinformatics↗

bioRxiv2023

Cell type matching across species using protein embeddings and transfer learning

MotivationKnowing the relation between cell types is crucial for translating experimental results from mice to humans. Establishing cell type matches, however, is hindered by the biological differences between the species. A substantial amount of evolutionary information between genes that could be used to align the species is discarded by most of the current methods since they only use one-to-one orthologous genes. Some methods try to retain the information by explicitly including the relation between genes, however, not without caveats. ResultsIn this work, we present a model to Transfer and Align Cell Types in Cross-Species analysis (TACTiCS). First, TACTiCS uses a natural language processing model to match genes using their protein sequences. Next, TACTiCS employs a neural network to classify cell types within a species. Afterwards, TACTiCS uses transfer learning to propagate cell type labels between species. We applied TACTiCS on scRNA-seq data of the primary motor cortex of human, mouse and marmoset. Our model can accurately match and align cell types on these datasets. Moreover, at a high resolution, our model outperforms the state-of-the-art method SAMap. Finally, we show that our gene matching method results in better matches than BLAST, both in our model and SAMap. Availabilityhttps://github.com/kbiharie/TACTiCS Contacta.mahfouz@lumc.nl

bioinformatics↗

bioRxiv2022

Predicting cell population-specific gene expression from genomic sequence

Most regulatory elements, especially enhancer sequences, are cell population-specific. One could even argue that a distinct set of regulatory elements is what defines a cell population. However, discovering which non-coding regions of the DNA are essential in which context, and as a result, which genes are expressed, is a difficult task. Some computational models tackle this problem by predicting gene expression directly from the genomic sequence. These models are currently limited to predicting bulk measurements and mainly make tissue-specific predictions. Here, we present a model that leverages single-cell RNA-sequencing data to predict gene expression. We show that cell population-specific models outperform tissue-specific models, especially when the expression profile of a cell population and the corresponding tissue are dissimilar. Further, we show that our model can prioritize GWAS variants and learn motifs of transcription factor binding sites. We envision that our model can be useful for delineating cell population-specific regulatory elements.

bioinformatics↗

bioRxiv2022

Single-cell reference mapping to construct and extend cell type hierarchies

Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups often using distinct terminology. Understanding how annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge. Whereas recent computational techniques, referred to as "reference mapping" methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e., queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present "treeArches", a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.

bioinformatics↗