همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Uncovering Disrupted Cell-Cell Interactions in Alzheimer's Disease Using Variational Graph Autoencoders on Single-Cell Spatial Transcriptomics Data from the Human Middle Temporal Gyrus

نویسندگان :

Zahra Bayat¹ Alireza Fotuhi Siahpirani²

1- Laboratory of Bioinformatics and Computational Genomics (LBCG), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran 2- Laboratory of Bioinformatics and Computational Genomics (LBCG), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran

کلمات کلیدی :

Middle Temporal Gyrus (MTG)،Alzheimer's Disease (AD)،Cell Cell interaction،Single-Cell Spatial Transcriptome،Variational Graph Autoencoder (VGAE)

چکیده :

The human middle temporal gyrus (MTG) is a susceptible brain region in the early stages of Alzheimer's disease (AD), yet the molecular mechanisms driving this regional vulnerability remain poorly understood. Understanding the intricate cell-cell interactions (CCIs) in this region is crucial for elucidating the pathophysiology of AD. Recent advances in single-cell spatial transcriptomics and deep learning have created new opportunities to explore CCIs in unprecedented detail. Variational graph autoencoders (VGAEs), a state-of-the-art deep generative model for graph-structured data, enable the encoding of complex interactions between cells while preserving their spatial and molecular context. Chen and colleagues (Chen et al. 2020) have used 10x Visium platform to measure spatial transcriptomics in AD and control MTG samples. The dataset was obtained from GEO (GSE220442) and analyzed with Seurat. Data were log-normalized, scaled, and integrated using reciprocal principal component analysis (RPCA) and the Louvain algorithm was used for dimensionality reduction and clustering. To employ the VGAE model for inferring cell-cell interactions, we used the DeepLinc pipline (Li & Yang 2022) with two graph convolutional layers, which requires two input files: the adjacency matrix of a cell-cell interaction graph (A) and a gene expression matrix (X) as features of the nodes in A. The adjacency matrix was created by using the K-nearest neighbors (KNN) algorithm to find the three closest neighbors for each cell based on geometric closeness, with a distance threshold applied to consider only nearby cells as direct contacts. The latent representations learned by DeepLinc were used to infer known and novel CCIs. Cell-type specific differentially expressed genes (DEGs) were identified using Seurat's FindMarkers function. Next, key cell type pairs were predicted by DeepLinc, and Ligand-Receptor (LR) analysis of the DEGs in these cell types was performed using the NicheNet (Browaeys et al. 2020). Subsequently, we utilized ClusterProfiler to perform Gene Ontology (GO) enrichment and KEGG pathway analysis for the dysregulated LR-associated genes. Our analysis reveals significant disruptions in CCIs in the MTG region of AD. We identified decreased astrocyte and microglial signaling to neurons, downregulated communication between excitatory and inhibitory neurons, and upregulated microglia-to-astrocyte and oligodendrocyte interactions. Additionally, abnormal interactions between endothelial cells and other brain cells were observed. Our integrative analysis identified key dysregulated LR interactions in a cell type-specifc manner and key biological functions and pathways associated with them. This study highlights the utility of VGAEs for analyzing spatially resolved single-cell transcriptomic data to restore disrupted cellular communication in AD by unsupervised integration of both the entire transcriptomic landscape and graph topology.

لیست مقالات

لیست مقالات بایگانی شده

Expansion and Sequencing of the DNA Code Used in the COVID-19 Vaccine Using Meta-Heuristic Algorithms

Ahmad Aliyari Boroujeni - Mohammadreza Parsayi - Hossein Rahmati

Comparative Analysis of Enzybiotic Gene Abundance Across Environmental Microbiomes with Varied Plastic Pollution Levels

Arad Ariaeenejad - Arman Hasannejad - Donya Afshar Jahanshahi - Mohammad Reza Zabihi - Shohreh Ariaeenejad - Kaveh Kavousi

Iron, Oxidative Stress, and Skin Aging: The Therapeutic Potential of TGF-β1 Modulation

Maryam Kaboli - Amir Amiri-Yekta - Seyed Abolhassan Shahzadeh Fazeli - Yasser Tahamtani

Age-Related Gene Expression Changes in Microglia: Insights from the Nygen platform and Brain-Aging Atlas

Anahita Esmaeili-Mehr - َAygin Zabtkar - Seyed abolhassan Shahzade Fazeli - Amir Amiri-Yekta - Yaser Tahmtani

Vaccine design for outer membrane protein C(Shigella Flexneri)

Maedeh Esmaili - Fatemeh Sefid

The key role of genes involved in proline biosynthesis and photosynthesis in drought response during Soybean flowering stage

Zeinab Shamabadi - Maryam Pasandideh Arjmand - Mohammad Mohsenzadeh Golfazani - Habibollah Samizadeh lahiji

Molecular docking and bioinformatics study of active compounds of thyme) Thymus vulgaris( in inhibiting COX-2 enzyme related to inflammatory diseases

Razieh Biglari Farash - Azizollah Kheiry - Najmaddin Mortazavi - Mohsen Sani khani

Comprehensive Analysis of DCLK Family Kinases in Gastrointestinal Cancers and Neoplastic Progression

Zahra Salehi - Leili Rejali - Moein Piroozkhah - Pooya Jalali - Ali Aghajani - Mahsa Saeedi Niyasar - Binazir Khanabadi - Amir Sadeghi - Ehsan Nazemalhossein Mojarad

Inhibition of angiogenesis based on the dynamic model of tumor growth using adaptive control method

Mehdi Ghasemi - Adel Akbarimajd - Solmaz Kia

Study population structure in Iranian Arab horse breed by principal component analysis (PCA) and discriminant analysis of principal components (DAPC) methods using genomic data

Behkam Teymori - Hossein Moradi sharbabak - Mohammad Moradi sharbabak - Mohammad Bagher Zandi - Alireza Fotuhi Siahpirani

بیشتر