Ahmed Essam Mahfouz

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

This study uses spatial transcriptomics to compare gene expression changes in brain regions of mdx and mdx4cv DMD mouse models, revealing distinct patterns linked to dystrophin isoform loss.

Image-guided alignment of consecutive multi-modal tissue slides

COAST aligns consecutive tissue sections using only spatial images, enabling unified multi-modal molecular data analysis without molecular markers or annotations.

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

The paper introduces SACCELERATOR, a framework for spatially aware clustering that improves benchmarking by incorporating expert feedback and consensus analysis across diverse datasets.

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

The paper uses spatial metabolomics and transcriptomics to identify persistent metabolic failure in kidney niches after ischemia-reperfusion injury, highlighting mitochondrial dysfunction in otherwise healthy areas.

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

This study uses spatial transcriptomics to evaluate exon-skipping therapies for DMD in the CNS, showing U7ex51 effectively restores full-length dystrophin isoforms in neurons.

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

GAZE predicts metabolite concentrations from gene expression using a physics-informed graph neural network, achieving better performance than existing methods in zero-shot scenarios.

Federated single-cell QTL meta-analysis reveals novel disease mechanisms

The paper performs a federated cis-eQTL meta-analysis across single-cell datasets to identify cell-type-specific genetic effects and link them to disease mechanisms.

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

The paper identifies FOXP1 as a key regulator of oxytocin neuron differentiation and shows its role in reducing AVP and OXT neuron abundance in knockout mice.

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

This paper introduces spVIPES, a framework for disentangling shared and private factors of variation in single-cell RNA-seq data, showing improved performance over existing batch correction methods.

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

This study integrates mass cytometry data from diverse populations to identify immune signatures associated with rural living, finding shared differences in lymphocyte and innate immune cell characteristics.

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

This paper introduces scAGG, a model for classifying Alzheimer's disease from single-nucleus RNA-seq data by aggregating single-cell embeddings, and finds it accurately classifies AD and healthy controls with disease severity ordering in the latent space.

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

The paper introduces gsQTL, a method that links genetic risk variants to gene sets by analyzing shared variability, showing improved performance over traditional approaches.

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

The paper predicts cell-type-specific exon inclusion in human brain cells using RBP binding sites and genomic sequence, revealing more complex splicing mechanisms in neurons than glia.

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

This study creates a detailed single-cell RNA-seq atlas of the mouse PVN, identifying transcriptional subtypes and linking them to functional roles.

Benchmarking Variational AutoEncoders on cancer transcriptomics data

This study benchmarks variational autoencoders on cancer transcriptomics data to evaluate hyperparameter effects and latent space biological relevance.

Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models

The paper uses spatial transcriptomics in DMD mouse models to identify gene expression signatures linked to histopathological changes in skeletal muscle.

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

The paper introduces treeArches, a framework for building and extending cell-type hierarchies by mapping new datasets to reference atlases and harmonizing annotations across studies.

Cell type matching across species using protein embeddings and transfer learning

This paper introduces TACTiCS, a model that matches cell types across species using protein embeddings and transfer learning, outperforming existing methods.

A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery

The paper builds a comprehensive mouse kidney atlas using scRNA-seq and snRNA-seq data to identify rare cell populations and their markers.

Single-cell RNA sequencing data reveals rewiring of transcriptional relationships in Alzheimer's Disease associated with risk variants

The paper uses scRNA-seq data to identify gene expression rewiring in Alzheimer's Disease linked to risk variants by analyzing differential correlations across cell types.

Transcriptional and cell type profiles of cortical brain regions showing ultradian cortisol rhythm dependent responses to emotional face stimulation

The paper examines how loss of ultradian cortisol rhythm affects emotional processing in cortical brain regions by analyzing transcriptional and cell type profiles.

scTopoGAN: Unsupervised manifold alignment of single-cell data

scTopoGAN aligns single-cell datasets with non-overlapping cells or features using topology-guided GANs, outperforming existing methods in unsupervised settings.

Predicting cell population-specific gene expression from genomic sequence

This paper introduces a model that predicts cell population-specific gene expression from genomic sequence, outperforming tissue-specific models and enabling GWAS variant prioritization and motif learning.

The rise of sparser single-cell RNAseq datasets; consequences and opportunities

The paper shows that binarized gene expression in scRNA-seq is as informative as count-based expression and scales better to larger datasets.

Correcting differential gene expression analysis for cyto-architectural alterations in substantia nigra of Parkinson's disease patients reveals known and potential novel disease-associated genes and pathways

scMoC: Single-Cell Multi-omics clustering

scMoC integrates scRNA-seq and scATAC-seq data to identify biologically meaningful cell clusters through RNA-guided imputation and cross-modal clustering.

Transcriptomic signatures associated with regional cortical thickness changes in Parkinson's disease

The paper identifies gene expression patterns linked to cortical thickness changes in Parkinson's disease using MRI and transcriptomic data, revealing pathways related to cellular maintenance mechanisms.

Intronization enhances expression of S-protein and other transgenes challenged by cryptic splicing

The paper shows that inserting short introns into transgenes improves their expression by reducing cryptic splicing, particularly for the SARS-CoV-2 Spike protein.

SIRV: Spatial inference of RNA velocity at the single-cell resolution

SIRV infers spatial RNA velocities at single-cell resolution by integrating spatial transcriptomics with scRNA-seq data, revealing spatial differentiation patterns in developing tissues.

Timing and localization of myasthenia gravis-related gene expression

The paper investigates the spatio-temporal expression of MG-related genes in human tissues and the CNS using public datasets, identifying new tissues and brain regions with high expression.