Tissue-origin transcriptomic interactions as indicators of inflammatory aging and aging gene discovery

Area of Science:

• Immunology
• Genetics
• Computational Biology

Background:

• Aging involves complex immunological and physiological changes, increasing disease risk.
• Identifying immunogenetic aging markers from high-dimensional transcriptomics is difficult due to data complexity.
• Gene-gene interactions are crucial, and their patterns change with age in a tissue-specific manner.

Purpose of the Study:

• To develop a novel method for identifying immunogenetic aging markers.
• To create a highly accurate, tissue-specific inflammatory aging clock.
• To explore potential interventions for age-related immune decline.

Main Methods:

• Converting single-cell RNA sequencing (scRNA-seq) data into "genomaps" representing spatial transcriptomic interactions.
• Leveraging genomaps to detect aging-associated changes in gene interaction patterns.
• Comparing the novel aging clock's performance against traditional methods (blood tests, miRNA, proteomics).

Main Results:

• A highly accurate, tissue-specific inflammatory aging clock was constructed using genomaps.
• The genomap-based clock significantly outperformed traditional aging prediction methods.
• An in silico study demonstrated potential intervention via the Map3k1/Map2k4/JNK pathway to reverse B cell aging.

Conclusions:

• Genomaps offer a powerful new strategy for analyzing complex transcriptomic data in aging research.
• The developed aging clock has broad implications for understanding and potentially mitigating age-related diseases.
• This framework may advance aging and developmental biology research and therapeutic interventions.