METTL3 Mediates Atheroprone Flow–Induced Glycolysis in Endothelial Cells

Medicine, Cardiovascular System, Life Sciences, Cell Biology, Biochemistry, Immunology,
glycolysis   endothelial cells   atherosclerosis   METTL3   flow   RNA modification   metabolic reprogramming  

1. Research Background Atherosclerosis is the primary pathological basis of cardiovascular diseases, closely associated with endothelial cell (EC) dysfunction. Hemodynamic factors play a critical role in the regional selectivity of atherosclerosis: oscillatory shear stress (OS) (e.g., at vascular bifurcations) promotes plaque formation, while pulsa...

Chrombus-XMBD: A Graph Convolution Model Predicting 3D-Genome from Chromatin Features

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Cell Biology, Genetics, Biophysics,
3D-genome   graph convolution model   chromatin features   long-range interaction   model generalizability  

Research Background and Disciplinary Significance In eukaryotic cells, the three-dimensional (3D) spatial structure of chromatin plays a crucial role in gene expression regulation. Through complex folding, looping, and local spatial reconfiguration of DNA, different genetic elements (such as promoters and enhancers) are brought into spatial proximi...

Deep scSTAR: Leveraging Deep Learning for the Extraction and Enhancement of Phenotype-Associated Features from Single-Cell RNA Sequencing and Spatial Transcriptomics Data

Information Science, Artificial Intelligence, Medicine, Life Sciences, Cell Biology, Immunology,
Single-cell RNA sequencing   Spatial transcriptomics   Deep learning   Phenotype-associated features   Tumor microenvironment  

In recent years, cutting-edge technologies such as single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have greatly advanced the development of life sciences and clinical medicine. These technologies have revealed cellular heterogeneity and brought novel insights into major fields such as disease, development, and immunity. Howe...

Benchmarking Copy Number Aberrations Inference Tools Using Single-Cell Multi-Omics Datasets

Information Science, Artificial Intelligence, Medicine, Oncology, Life Sciences, Cell Biology, Bioengineering, Genetics,
Single-cell RNA sequencing   Copy number aberrations   Tumor heterogeneity   Benchmarking   Multi-omics  

1. Research Background and Significance In the fields of oncology and genomics, chromosomal copy number alterations (CNAs) are a key type of genetic variation driving the occurrence and progression of cancer. CNAs not only determine tumor heterogeneity but also play a crucial role in early tumor detection, subclone evolution analysis, research on d...

Unveiling a Novel Cancer Hallmark by Evaluation of Neural Infiltration in Cancer

Medicine, Oncology, Life Sciences, Cell Biology, Immunology, Genetics,
neural infiltration   cancer hallmark   tumor microenvironment   single-cell RNA sequencing   immunotherapy  

Cancer, as a major global public health challenge, is characterized by complex mechanisms underlying its onset and progression. For a long time, processes within the tumor microenvironment (TME)—such as immunity, inflammation, and angiogenesis—have been extensively studied and considered key determinants of tumor biological behavior. In recent year...

Inferring Gene Regulatory Networks from Time-Series scRNA-Seq Data via Granger Causal Recurrent Autoencoders

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Cell Biology, Genetics,
scRNA-seq   gene regulatory network   unsupervised learning   Granger causality   recurrent variational autoencoder  

1. Academic Background and Research Motivation In recent years, single-cell RNA sequencing (scRNA-seq) has become one of the most groundbreaking technologies in life sciences and medical research, enabling researchers to capture subtle differences in transcript levels among numerous cells at the resolution of individual cells. This technology has g...