Single-Cell Analysis – News and Features

Functional genomics can help deconvolute the link between genotype and phenotype in disease, revealing new drug targets and progressing understanding of existing drugs.

Research from VIB-UGent identifies lipid-associated macrophages and LAM-like Kupffer cells as critical for liver repair. The study reveals TREM2's role in clearing damaged cells and promoting recovery.

Self-supervised AI could offer a promising approach for analyzing 20 million cells or more.

This article explores how broadly neutralizing antibodies are being researched, not only for their potential in the prevention of HIV, but also for other rapidly mutating viruses such as influenza and SARS-CoV-2.

Combining long-read DNA sequencing with a predictive artificial intelligence model has provided new insights into DNA replication.

New insights into fibrolamellar hepatocellular carcinoma could lead to new drugs and significantly improved health outcomes.

Garvan Institute researchers discovered how chronic hepatitis C virus (HCV) infections lead to autoimmune diseases like cryoglobulinemic vasculitis. By identifying rogue B cell clones caused by persistent antibody clustering and mutations.

A machine learning algorithm has been developed to deliver more accurate results from single-cell gene expression database analysis.

A new framework has used neural network training dynamics to interpret single-cell and spatial omics data.

Most diabetic wound care products are passive dressings, only providing a protective barrier against the external environment. This article explores how hydrogel technologies can promote diabetic wound healing.