The three-dimensional analysis of cell types and their locations by spatial transcriptomics provides key information of their interactions within tissues or organs. Based on this technology, ...

Single-cell transcriptomics has revolutionized the study of cellular diversity and function by enabling gene expression ...

The brain's immune cells removed plaques and helped restore a healthier environment in the brains of immunized patients. For over 30 years, scientists have focused on treating Alzheimer’s disease by ...

A new method of examining gene expression patterns called landscape transcriptomics may help pinpoint what causes bumble bees stress and could eventually give insight into why bee populations are ...

Spatial transcriptomics is a cutting-edge technique that characterizes gene expression within sections of tissue, such as ...

Transcriptomics is the study of the transcriptome—the complete set of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell—using high-throughput ...

Illumina has announced the development of an advanced spatial transcriptomics technology, with commercial availability expected in 2026. This next-generation solution is designed to enhance ...

Cyprotex has joined forces with parent company, Evotec, a leader in the field of transcriptomics. Understanding the role of transcriptomics in drug-induced toxicity is a key focus for the team.

Complete Genomics, a leading innovator in genomic sequencing, announced at the NextGen Omics & Spatial Biology Conference that it has partnered with BioTuring, developer of advanced bioinformatics ...

Spatial transcriptomics (ST) techniques, which have been rapidly evolving over the past decade, allow scientists to map gene activity within tissues while keeping their structure intact ...

Notably, microglia spatially associated with amyloid plaques exhibited increased expression of TREM2 and ApoE compared to ...

This figure shows how the STAIG framework can successfully identify spatial domains by integrating image processing and contrastive learning to analyze spatial transcriptomics data effectively.