bioRxivGenomicsPreprint
Neotenic transcriptomic features in the adult turquoise killifish brain
Ana María Burgos-RuiziD, Silvia NaranjoiD, Maria del Valle Hernández-Pérez, Lorena Ardila‐García, Marta Moreno-OñateiD, Ana Fernández‐MiñániD, Alejandro de los Reyes-Benitez, Javier López-Rı́osiD, Gretel NusspaumeriD, Juan J. TenaiD
Abstract
Aging is one of the main challenges facing modern society. Understanding the cellular processes that occur in the later stages of life is essential to address age-related diseases. Although aging is a global process that affects the entire body, the brain is one of the most sensitive organs and is affected by numerous degenerative diseases. In this study, we investigate cell types and genes that are particularly sensitive to the aging process. To this end, we performed a time-course single-cell RNAseq experiment series in the brain of the killifish, the vertebrate model with the shortest lifespan, which makes it an excellent model for aging studies. Our analysis reveals that non-glial progenitor cells are among those populations that change the most between young and old animals. Furthermore, we identify specialized stromal clusters that seem to support a primitive hematopoietic program in the brain, which is active only during the embryonic stages in other vertebrate species. Our results show that the expression of embryonic genes in the adult brain appears to be a general feature in killifish, and that several cell populations in the adult killifish brain show a higher level of similarity to the zebrafish embryonic populations than to adult ones. Our study suggests that adult killifish maintain a neotenic gene expression status in the brain that may help in sustaining their characteristic high proliferation and metabolic rates, as well as combat the detrimental effects of this high metabolism on cells, especially at advanced ages.
- organism: zebrafish
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
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