Back

Divergent somatic mutation patterns among human cerebellar neuron types

Gronska-Peski, M.; Srinivasa, A.; Evrony, G. D.

2025-09-30 genomics
10.1101/2025.09.29.679392 bioRxiv
Show abstract

Neurons in the human brain accumulate somatic mutations with age. However, it is largely unknown how somatic mutation rates and patterns vary among the brains diverse types of neurons. Characterizing this variability is critical for elucidating the role of genome integrity in human brain function and disease. Moreover, the significant physiological differences among the brains cell types provides an opportunity to learn more general underlying factors that determine mutation rates and patterns. Here, we utilized high-fidelity duplex DNA sequencing to profile somatic mutation processes across the lifespan in the two major cell types of the human cerebellum, Purkinje neurons and granule neurons, which have dramatically different sizes, functions, and physiologies. Surprisingly, these cell types exhibited similar rates of substitution mutations, including similar rates of signature SBS5 that is responsible for most mutations in the body yet whose mechanism remains unknown. However, we identified differences in Purkinje and granule neurons patterns of substitutions and in their rates and patterns of insertions and deletions, with transcription playing a key role in mediating these differences. Our work indicates that different types of neurons in the brain can differ in their aging-related somatic mutation processes. Our results further suggest that key features that distinguish Purkinje neurons from granule neurons, such as cell size, metabolic rates, and neuronal firing rates, are unlikely to be intrinsic determinants of the total substitution mutation rate and of signature SBS5, which is the most prevalent aging-related mutational process.

Published in Neuron · training set

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.