# Identification of markers for neurescence through transcriptomic profiling of postmortem human brains

## TL;DR

This study identifies reliable markers for neuronal senescence in human brains using transcriptomic data, revealing genes linked to neurodegeneration and mitochondrial dysfunction.

## Contribution

The study introduces a novel eigengene-based approach to identify neurescent markers with high accuracy using combined gene panels.

## Key findings

- Combining CDKN2D and ETS2 in a decision tree achieved 99% accuracy and 100% specificity in identifying neurescent cells.
- 324 genes were found to be overexpressed in neurescent cells, associated with Alzheimer’s, Parkinson’s, and Huntington’s diseases.
- Overexpressed genes are linked to mitochondrial dysfunction and cytoskeletal dysregulation.

## Abstract

Neuronal senescence (i.e., neurescent) is an important hallmark of aging and neurodegeneration, but it remains poorly characterized in the human brain due to the lack of reliable markers. This study aimed to identify neurescent markers based on single-nucleus transcriptome data from postmortem human prefrontal cortex. Using an eigengene approach, we integrated three gene panels: a) SenMayo, b) Canonical Senescence Pathway (CSP), and c) Senescence Initiating Pathway (SIP), to identify neurescent signatures. We found that paired markers outperform single markers; for instance, by combining CDKN2D and ETS2 in a decision tree, a high accuracy of 99% and perfect specificity (100%) were achieved in distinguishing neurescent. Differential expression analyses identified 324 genes that are overexpressed in neurescent. These genes showed significant associations with important neurodegeneration-related pathways including Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease. Interestingly, several of these overexpressed genes are linked to mitochondrial dysfunction and cytoskeletal dysregulation. These findings provide valuable insights into the complexities of neurescent, emphasizing the need for further exploration of histologically viable markers and validation in broader datasets.