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Single-cell transcriptomics and mouse model phenotyping for biomarker screen of peripheral blood biomarkers in Huntington disease

Martin-Climent, P.; Ortuno-Miquel, S.; Gallego-Serna, J. F.; Marti-Martinez, S.; Valor, L. M.

2025-12-16 neuroscience
10.64898/2025.12.14.694089 bioRxiv
Show abstract

Transcriptional dysregulation is among the most prominent molecular alterations in Huntingtons disease (HD). It is not confined to brain but is also extended to peripheral tissues and cells, enabling minimally invasive screens aimed at identifying transcriptional surrogates of the health status in HD mutation carriers. Nonetheless, transcriptomics approaches have failed to identify consistent candidates from peripheral blood, probably due to the low impact of the HD mutation in the transcriptional profiles of circulating cells that can be masked by the high cellular complexity of this biofluid. In this study, we applied for the first time single-cell RNA-seq in peripheral blood mononuclear cells (PBMCs) to determine those cells that accumulate the most prominent gene expression changes as potential sources of reliable biomarkers. We observed both specific and common transcriptional alterations across different blood cell subtypes, justifying partial overlapping of our results with published bulk transcriptomics datasets. To relate these gene expression patterns with disease progression in the absence of a large cohort of patients, we examined selected candidates in a phenotypically characterized cohort of transgenic R6/1 mice. Of the tested genes, only the changes in the interferon related gene Irf7 in blood were correlated with motor coordination performance in mutant mice. Notably, this gene was significantly upregulated in the striatum of these animals. Overall, transcriptional-based changes in peripheral blood can be linked to HD although dissection of individual blood cells in combination with subsequent validation in mouse models emphasizes the challenges in obtaining clinically relevant biomarkers in HD.

Published in Scientific Reports (predicted rank #4) · training set

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