Reference gene variability across age and sex in 5XFAD mice highlights normalization challenges in Alzheimer’s models

Gene expression profiling in neurodegenerative diseases such as Alzheimer’s Disease (AD) is frequently performed using real-time quantitative polymerase chain reaction (RT-qPCR). The accuracy of this technique relies heavily on selecting suitable reference genes (RGs) for normalization and internal control. Ideally, RGs should maintain consistent transcription levels, unaffected by cellular or pathological changes. However, identifying stable RGs is challenging, particularly in diseases like AD, where gene expression fluctuates across disease stages. This study aimed to determine the most stable RGs in two brain regions - cortex (CTX) and hippocampus (HIPP) - of male and female 5XFAD mice, a model of familial AD. The stability of five commonly used RGs namely Gapdh, Ppia, Rer1, Rpl27 and Rps29, was evaluated using GeNorm, NormFinder, BestKeeper, and EndoGene algorithms across three AD stages: prodromal (2 months of age, mo), early (4 mo), and late (7 and 10 mo). Results revealed region-, sex-, and time-dependent differences in RG expression stability, reflecting the distinct vulnerability of CTX and HIPP to Aβ pathology. Using the two most stable RGs for normalization improved precision, reducing variability and increasing the significance level in target gene expression analyses. These findings emphasize the necessity of validating RGs under specific experimental conditions to ensure reliable RT-qPCR quantification in AD research.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-02-04
DOI
https://doi.org/10.1038/s41598-026-38346-5
Primary Topic
Molecular Biology Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reference gene variability across age and sex in 5XFAD mice highlights normalization challenges in Alzheimer’s models

Eleonora Daini, Michele Zoli, Martina Bodria, Antonietta Vilella et al.
Scientific Reports
Molecular Biology Techniques and Applications
article

Reference gene variability across age and sex in 5XFAD mice highlights normalization challenges in Alzheimer’s models

Eleonora Daini, Michele Zoli, Martina Bodria, Antonietta Vilella, Monica Piemontese, Kristy Antonioni
article en

Abstract

Gene expression profiling in neurodegenerative diseases such as Alzheimer’s Disease (AD) is frequently performed using real-time quantitative polymerase chain reaction (RT-qPCR). The accuracy of this technique relies heavily on selecting suitable reference genes (RGs) for normalization and internal control. Ideally, RGs should maintain consistent transcription levels, unaffected by cellular or pathological changes. However, identifying stable RGs is challenging, particularly in diseases like AD, where gene expression fluctuates across disease stages. This study aimed to determine the most stable RGs in two brain regions - cortex (CTX) and hippocampus (HIPP) - of male and female 5XFAD mice, a model of familial AD. The stability of five commonly used RGs namely Gapdh, Ppia, Rer1, Rpl27 and Rps29, was evaluated using GeNorm, NormFinder, BestKeeper, and EndoGene algorithms across three AD stages: prodromal (2 months of age, mo), early (4 mo), and late (7 and 10 mo). Results revealed region-, sex-, and time-dependent differences in RG expression stability, reflecting the distinct vulnerability of CTX and HIPP to Aβ pathology. Using the two most stable RGs for normalization improved precision, reducing variability and increasing the significance level in target gene expression analyses. These findings emphasize the necessity of validating RGs under specific experimental conditions to ensure reliable RT-qPCR quantification in AD research.

Scientific ReportsVol. 16(1)
University of Modena and Reggio Emilia (IT)
Openalex Percentile: Top 68%
Molecular Biology Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.