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Evaluation of suitable reference genes for gene expression studies in the developing mouse cortex using RT-qPCR

BMC Neuroscience volume 26, Article number: 12 (2025) Cite this article

Abstract

Background

Real-time quantitative PCR (RT-qPCR) is a widely used method to investigate gene expression in neuroscience studies. Accurate relative quantification of RT-qPCR requires the selection of reference genes that are stably expressed across the experimental conditions and tissues of interest. While RT-qPCR is often performed to investigate gene expression changes during neurodevelopment, few studies have examined the expression stability of commonly used reference genes in the developing mouse cortex.

Results

Here, we evaluated the stability of five housekeeping genes, Actb, Gapdh, B2m, Rpl13a, and Hprt, in cortical tissue from mice at embryonic day 15 to postnatal day 0 to identify optimal reference genes with stable expression during late corticogenesis. The expression stability was assessed using five computational algorithms: BestKeeper, geNorm, NormFinder, DeltaCt, and RefFinder. Our results showed that B2m, Gapdh, and Hprt, or a combination of B2m/Gapdh and B2m/Hprt, were the most stably expressed genes or gene pairs. In contrast, Actb and Rpl13a were the least stably expressed.

Conclusion

This study identifies B2m, Gapdh, and Hprt as suitable reference genes for relative quantification in RT-qPCR-based cortical development studies spanning the period of embryonic day 15 to postnatal day 0.

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Introduction

The development of the mammalian cerebral cortex is a highly complex process that involves elaborate sequences of events governed by precise molecular signaling and gene expression. In mice, corticogenesis begins on embryonic day (E) 10, when neural progenitor cells begin to proliferate and differentiate [1]. This is followed by neuronal migration, which ultimately gives rise to the six cortical cell layers that underpin cognition and higher brain functions [2]. Pups are typically born on E19-21, and the cortical circuits continue to mature and refine after birth [3]. Although the processes of cortical development are well-described, the specific mechanisms of gene regulation in healthy development and disease are not fully understood and remain an active area of research.

Despite the emergence of high-throughput gene profiling technologies, such as microarrays and RNA sequencing, real-time quantitative PCR (RT-qPCR) remains a widely used method for highly sensitive and accurate mRNA quantification. RT-qPCR data is commonly analyzed using relative quantification methods, wherein the expression of a target gene is measured relative to a reference gene within that sample [4, 5]. For accurate normalization, the reference gene must be expressed at constant levels across experimental conditions. To this end, housekeeping genes are often used since they are essential for basic cellular functions and are presumed to be stably expressed across cells and conditions [6]. However, studies have shown that some housekeeping genes can change expression in response to various conditions [7], emphasizing the importance of validating the expression stability of reference genes in specific cell types, tissues, organisms, and conditions of interest. In neuroscience, such reference gene validation studies have been performed in rodent brain tissue from a wide range of conditions, including various brain diseases [8,9,10,11,12,13,14,15,16], injuries [17,18,19,20,21,22], tumors [23], stroke and hypoxia-ischemia [24,25,26,27], infection [28, 29], inflammation [30], drugs and alcohol exposure [31,32,33], toxin exposure [34,35,36], hormone therapy [37, 38], pregnancy and reproduction [39, 40], and diet [41,42,43]. Several studies have also assessed reference gene stabilities in the rodent brain during various embryonic and postnatal periods [44,45,46,47,48,49].

In this study, we evaluated the stability of five common housekeeping genes, β-actin (Actb), Glyceraldehyde-3-phosphate dehydrogenase (Gapdh), β2 microglobulin (B2m), Ribosomal protein L13a (Rpl13a), and Hypoxanthine–guanine phosphoribosyl transferase (Hprt) during late cortical development using RT-qPCR. Whereas early corticogenesis (E10-15) is associated with critical cell proliferation and peak neurogenesis, late corticogenesis (E15-P0) reflects an important time window for neuronal migration, maturation, and integration into cortical networks [1,2,3]. Disruption of these later developmental processes has been associated with many cognitive and neuropsychiatric disorders [50,51,52,53,54]. To identify stable reference genes that are suitable for RT-qPCR studies spanning late corticogenesis in mice, we assessed the expression stability of Actb, Gapdh, B2m, Rpl13a, and Hprt in cortical tissue from E15, E17, and postnatal day (P) 0 mice using five popular computational algorithms– BestKeeper [55], geNorm [56], NormFinder [57], DeltaCt [58], and RefFinder [59, 60]. Overall, we identified B2m, Gapdh, and Hprt as the most stably expressed genes, and Actb and Rpl13a as the least stably expressed genes, in E15 to P0 cortical tissue.

Methods

Animal tissue collection

All animal procedures were approved by The University of Texas at Dallas Institutional Animal Care and Use Committee (IACUC) and performed following the NIH Guide for the Care and Use of Laboratory Animals. All efforts were made to minimize suffering.

Three E14 time-pregnant CD-1 mice were purchased from Charles River Laboratories (USA) and housed in light-, temperature-, and humidity-controlled rooms on a 12:12 light/dark cycle. The dams were randomly assigned to either the E15, E17, or P0 groups, and cortical tissue from six littermate offspring was collected from each dam at the specified ages. For the E15 and E17 timepoints, pregnant mice were deeply anesthetized with isoflurane and decapitated. Embryos were removed from the abdominal cavity, and whole cortical hemispheres were rapidly dissected in ice-cold phosphate-buffered saline (PBS; 137mM NaCl, 2.7mM KCl, 10 mM Na2HPO4, 1.8mM KH2PO4, pH 7.4) under a stereomicroscope (Motic SMZ 1717). For the P0 timepoint, pups were anesthetized with isoflurane, decapitated, and whole cortical hemispheres were dissected as described above. Isolated tissues were immediately flash frozen in dry ice and stored at -80 °C until used. Each biological replicate consisted of one whole cortical hemisphere from an individual animal. All animals within each age group were from the same litter.

Total RNA isolation and quality control

Total RNA was extracted from whole cortical hemispheres using the RNeasy Plus Universal Mini Kit (Qiagen 73404) following the manufacturer’s protocol. Tissues were homogenized using QIAzol Lysis Reagent by passing the tissue through a 27-gauge needle and syringe. Genomic DNA was removed using the gDNA Eliminator Solution included in the kit. Purified RNA was eluted from the spin column with 60 µl RNase-free water and stored at -80 °C.

Total RNA concentration and quality were measured using the NanoDrop One UV-vis spectrophotometer (Thermo Scientific). The RNA yields ranged from 12.0 to 15.9 µg (E15), 15.9–26.1 µg (E17), and 34.7–52.7 µg (P0). The A260/A280 absorbance ratios were 2.0-2.2 and the A260/A230 ratios were 0.4–2.3 for all samples. The lower-than-expected A260/A230 values were found in some of the E15 and E17 samples due to trace presence of guanidinium thiocyanate from the extraction reagents. In these samples, the guanidinium thiocyanate was present at a concentration of < 15 mM. Per the RNA extraction kit manufacturer’s guidelines, guanidine thiocyanate concentrations of < 100 mM in RNA samples are not thought to compromise RT-qPCR reliability (https://www.qiagen.com/us/resources/faq?id=c59936fb-4f1e-4191-9c16-ff083cb24574%26;lang=en), and therefore, the samples were deemed acceptable for downstream experiments. RNA integrity was assessed by denaturing agarose gel electrophoresis. The RNA samples were run on a 1% agarose gel stained with ethidium bromide and visualized using the ChemiDoc Imaging System (Bio-Rad). All samples displayed sharp 28 S and 18 S ribosomal RNA bands, indicating intact RNA.

cDNA synthesis

First-strand cDNA synthesis was performed using the iScript gDNA Clear cDNA Synthesis Kit (Bio-Rad 172–5034) following the manufacturer’s protocol. For each sample, 500 ng total RNA was treated with the kit’s DNase reagent, followed by incubation at 25 °C for 5 min (DNA digestion) and 75 °C for 5 min (DNase inactivation) to eliminate genomic DNA. DNAse-treated RNA samples were then converted to cDNA using the iScript Reverse Transcription Supermix (or the iScript no-Reverse Transcription Supermix for no reverse transcription controls). cDNA synthesis was performed on a PT100 thermocycler (Bio-Rad) using the following thermal protocol: 25 °C for 5 min (priming), 46 °C for 20 min (reverse transcription), and 95 °C for 1 min (reverse transcription inactivation). The cDNA samples were stored at -20 °C.

Real-time quantitative PCR (RT-qPCR)

RT-qPCR was performed using 96-well PrimePCR Custom Plates (Bio-Rad 10025216). Each plate contained pre-designed PrimePCR primer assays for 5 housekeeping genes: Actb, Gapdh, B2m, Rpl13a, and Hprt. The primers were previously tested and validated for amplification specificity, efficiency, linear dynamic range, and background signal by the manufacturer to meet the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines [61]. The list of evaluated candidate reference genes and primer characteristics are listed in Table 1.

Table 1 List of candidate reference genes and primer characteristics
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The RT-qPCR reactions were carried out using the SsoAdvanced Universal SYBR Green Supermix (Bio-Rad 172–5270), which contains the antibody-mediated hot-start Sso7d fusion polymerase and optimized buffers for maximum efficiency and sensitivity, following the manufacturer’s protocol. Each reaction contained 15 ng cDNA (equivalent to 3% input RNA) in a 20 µl reaction volume. RT-qPCR was performed on a CFX Duet Real-Time PCR System (Bio-Rad) using the following thermal protocol: 95 °C for 2 min (activation), followed by 40 cycles of 95 °C for 5 s (denaturation) and 60 °C for 30 s (annealing/extension). A final melting curve protocol was performed for all plate runs: 95 °C for 5 s, followed by an increase from 65 °C to 95 °C at 0.5 °C increments in 5 s.

The experiment was run on two 96-well plates. Each plate contained 3 biological replicates (i.e., 3 mice) per age group with 2 technical replicates per biological replicate (final sample number n = 6 mice per group). No reverse transcription and no template controls were included for all experiments.

Data analysis and statistics

The qPCR amplification curves, melting curves, and Ct values were analyzed with the CFX Maestro software (Bio-Rad) and imported into Microsoft Excel. The Ct values for each biological replicate (i.e., each mouse) were obtained by averaging the values from the two technical replicates. The Ct and standard deviation (SD) data were organized into all samples combined and as separate age groups for each gene (Table 2). Differences in Ct values between the age groups were analyzed by one-way ANOVA with Tukey’s post-hoc test using GraphPad Prism 10 software. The significance value was set at p < 0.05.

Table 2 Mean Ct values and standard deviation of candidate reference genes
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Gene expression stability analyses were performed by inputting raw Ct values into the web-based RefFinder tool (https://www.ciidirsinaloa.com.mx/RefFinder-master/). RefFinder performs stability analysis using the BestKeeper [55], geNorm [56], NormFinder [57], and DeltaCt [58] algorithms and provides a comprehensive stability ranking based on the geometric mean of the ranks from each program. Data generated by RefFinder were individually confirmed using the following Excel-based tools for each specific program: BestKeeper v1.0 (https://www.gene-quantification.de/bestkeeper.html), GeNorm v3 (https://genorm.cmgg.be), and NormFinder v0953 (https://www.moma.dk/software/normfinder). RefFinder’s DeltaCt results were manually calculated and confirmed using Excel. All data were analyzed at 100% amplification efficiency. The algorithmic basis and criteria for ranking in each program are further explained in the Results.

Results

Selection of candidate reference genes, qPCR amplification, and primer specificity

The five candidate reference genes in this study were chosen based on their cellular functions and popular use in neuroscience research. We selected housekeeping genes with different functional classes, including cytoskeleton structure (Actb), metabolism (Gapdh, Hprt), major histocompatibility complex class I component (B2m), and protein synthesis (Rpl13a), to diversify the list of candidate reference genes and to minimize the chance that the evaluated genes are co-regulated (see Table 1).

All evaluated genes amplified with Ct values between 17 and 28 across all samples (Table 2; see Supplementary Fig. 1a, c for amplification curves). Melt curve analyses revealed single peaks for all PCR reactions at the expected primer melting temperatures, indicating the presence of single amplicons in the reactions (Supplementary Fig. 1b, d). No amplifications were detected in the no reverse transcription and no template controls, verifying the absence of genomic DNA contamination and non-specific amplification. The raw Ct values for all samples and negative controls are available in Supplementary Tables 1–4.

Ct values for candidate reference genes

To examine the expression levels of the selected genes, we evaluated the Ct values for each gene in the combined E15, E17, and P0 age groups (Fig. 1a; Table 2). The mean Ct values ranged from 18.99 to 26.01, verifying abundant target gene expression in the cortical tissue samples. Among these genes, Actb (mean Ct = 18.99), Gapdh (mean Ct = 20.58), and Rpl13a (mean Ct = 19.58) were the most abundant, while B2m (mean Ct = 26.01) and Hprt (mean Ct = 25.07) were the least abundant (Fig. 1a; Table 2). Rpl13a had the smallest expression level variation (SD = 0.53) with a maximum difference of 2 cycles across samples, while Actb had the largest variation (SD = 1.14) with a maximum difference of 3.5 cycles across samples (Fig. 1a; Table 2).

Fig. 1
figure 1

Cycle threshold (Ct) values of candidate reference genes in E15 to P0 mouse cortical tissue. (a) Box-and-whisker plot showing the distribution of Ct values for candidate reference genes in the combined age groups. n = 18 mice per gene. (b) Box-and-whisker plot showing Ct values of candidate reference genes in the individual age groups. n = 6 mice per age group. For each candidate gene, the differences in Ct values between the age groups were analyzed by one-way ANOVA. Where the ANOVA was significant, a Tukey’s post-hoc test was applied. Significant Tukey’s pairwise comparisons are denoted with a star: *p < 0.05 (Rpl13a E15 vs. P0). The line inside the box indicates the median, the boxes represent the 25th and 75th percentiles, and the whiskers show the minimum and maximum values. The dots represent values from individual animals. Ct, cycle threshold

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To test whether the mean Ct values were different between age groups, we separated the data according to age and performed a one-way ANOVA with Tukey’s post-hoc test to assess group differences (Fig. 1b; Table 2). We found a small but significant increase in the mean Ct values of Rpl13a between E15 and P0 [F (2, 15) = 4.1720, p = 0.0363, Tukey’s post-hoc test: p < 0.05, Fig. 1b]. No significant age-dependent changes were observed in the other genes [Actb: F (2, 15) = 0.0231, p = 0.9772, Gapdh: F (2, 15) = 0.0354, p = 0.9653, B2m: F (2, 15) = 0.8920, p = 0.4305, Hprt: F (2, 15) = 0.8154, p = 0.4612, Fig. 1b].

Expression stability of candidate reference genes

To determine the expression stability of the candidate reference genes, we applied the BestKeeper, geNorm, NormFinder, DeltaCt, and RefFinder algorithms. These algorithms calculate an expression stability measure based on Ct values and rank each gene from least to most stable based on the measures. The stability ranking results from each program are plotted in Fig. 2.

Fig. 2
figure 2

Stability rankings according to BestKeeper, geNorm, NormFinder, DeltaCt, and RefFinder. Bar graphs showing gene expression stability rankings according to (a) BestKeeper, (b) geNorm, (c) NormFinder (without group identifiers), (d) NormFinder (with group identifiers), (e) DeltaCt, and (f) RefFinder. Genes are organized from the most to least stable. The stability values for each gene as calculated by each algorithm are shown on top of the bars. MAD, average absolute deviation; M value, stability measure; S value, stability value; SDavg, mean standard deviation; GMR, geometric mean of the rankings

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BestKeeper calculates a set of descriptive statistics, including the arithmetic mean Ct, the geometric mean Ct, the minimal and maximal Ct values, the mean absolute deviation (MAD) from the arithmetic mean, and the coefficients of variance for each candidate reference gene [55]. The extreme values of over- or under-expression levels, expressed as minimum or maximum x-fold compared to the geometric mean Ct, and the x-fold absolute deviation are also calculated. The descriptive statistics of the current dataset are listed in Table 3. BestKeeper uses MAD to rank the genes from the most to least stable expression. Genes with lower MAD have higher stability, whereas those with MAD > 1 are unstable [55]. By this approach, the most stable gene was Rpl13a (MAD = 0.413), followed by B2m (MAD = 0.452), Hprt (MAD = 0.503), and Gapdh (MAD = 0.762). Actb had a MAD of 1.034 and was considered unstable (Fig. 2a).

Table 3 Ct descriptive statistics obtained by BestKeeper
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geNorm calculates a stability measure (M value) based on the principle that two ideal reference genes should have identical expression ratios in all samples, regardless of experimental conditions or tissue type [56]. For each candidate reference gene, the pairwise variation (as determined by the SD of logarithmically transformed expression ratios) between that gene and other tested genes is calculated, and the stability value, M, is determined based on the average pairwise variations. A lower M value indicates greater stability. Stepwise exclusion of the least stable gene (i.e., highest M value) is iteratively performed to rank the tested genes until the two most stable genes are identified. An M value of ≤ 1.5 indicates stable expression, although stably expressed genes usually display an M value < 0.5. Based on this analysis, B2m and Rpl13a were identified as the two most stable genes (M = 0.293), followed by Hprt (M = 0.423) and Gapdh (M = 0.456). Actb was the least stable gene (M = 0.529) (Fig. 2b).

NormFinder calculates a stability value (S) for each candidate reference gene based on a mathematical model that estimates both the inter- and intra-group expression variation of candidate reference genes in a sample set [57]. When the sample set was analyzed as one group (i.e., without group identifiers), Gapdh was the most stable gene (S = 0.159), followed by B2m (S = 0.202), Hprt (S = 0.355), and Rpl13a (S = 0.494). Actb was the least stable gene (S = 0.580) (Fig. 2c). When both inter- and intra-group variations were considered, Gapdh (S = 0.125) and B2m (S = 0.157) remained the two most stable genes, followed by Hprt (S = 0.282) and Actb (S = 0.306). Rpl13a (S = 0.309) was the least stable gene, consistent with the observations that the mean Ct significantly increases between E15 and P0 (Fig. 2d). Studies have suggested using a combination of reference genes rather than a single reference gene for more robust target gene normalization [56]. To this end, NormFinder also calculates a combined stability value for each gene pair to determine the combination of two genes with the highest stability [57]. Here, Gapdh and B2m were identified as the gene combination with the highest combined stability value (S = 0.11), followed by B2m and Hprt (S = 0.16) (Table 4).

Table 4 Combined stability values of gene pairs obtained by NormFinder
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DeltaCt selects the most stable reference genes by comparing the relative expression (delta Ct) of pairs of genes within a tissue sample [58]. For each candidate reference gene, the mean SD (SDavg) derived from a set of pairwise comparisons between the candidate gene and other tested genes is used to rank stability. Genes with lower SDavg are considered more stable. By this method, the most stable genes were Gapdh (SDavg = 0.451) and B2m (SDavg = 0.451), followed by Hprt (SDavg = 0.527) and Rpl13a (SDavg = 0.560). Actb had the lowest stability (SDavg = 0.639) (Fig. 2e).

To incorporate all the results from Bestkeeper, geNorm, NormFinder, and DeltaCt, we performed a final comprehensive ranking using RefFinder [59, 60]. The RefFinder algorithm compares the rankings from the four aforementioned programs and assigns an appropriate weight to each gene. The geometric mean of their rankings (GMR) is then calculated and an overall ranking is provided. Genes with lower GMR have more stable expression. Using this integrated approach, B2m was identified as the most stable gene (GMR = 1.682), followed by equally ranked Gapdh (GMR = 2.000) and Rpl13a (GMR = 2.000), and Hprt (GMR = 3.000). Actb was identified as the least stable gene (GMR = 5.000), consistent with the results from the individual programs (Fig. 2f). The expression stability values and rankings according to each algorithm are summarized in Table 5.

Table 5 Stability values and rankings according to BestKeeper, geNorm, NormFinder, DeltaCt, and RefFinder
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Discussion

In this study, we evaluated the suitability of Actb, Gapdh, B2m, Rpl13a, and Hprt as reference genes in E15 to P0 mouse cortical tissue samples using RT-qPCR and computational algorithms to analyze expression stability. We found that B2m, Gapdh, and Hprt, or a combination of B2m/Gapdh and B2m/Hprt, had the highest expression stability in the cortex during the period of E15 to P0, and we recommend them as reliable reference genes for studying mouse late corticogenesis. We caution against using Actb and do not recommend using Rpl13a due to low stability and high expression variability.

Several computational algorithms have been developed to identify optimal reference genes based on non-normalized expression stability rankings, including BestKeeper [55], geNorm [56], NormFinder [57], and DeltaCt [58]. The underlying principle of these programs is that genes with the most stable expression across samples and conditions are the best references. However, due to differences in methodologies, these algorithms may produce different stability rankings. Therefore, a newer fifth algorithm, RefFinder, was developed to reconcile the differences by providing a comprehensive ranking based on the geometric mean of weighted rankings from the previous algorithms [59, 60]. In the present study, RefFinder identified B2m as the most stable gene among the evaluated genes, followed by equally ranked Gapdh and Rpl13a, and then Hprt. B2m was consistently ranked in the top two genes across all algorithms. Gapdh was ranked the most stable gene by NormFinder and DeltaCt, but second to lowest by BestKeeper and GeNorm. Conversely, Rpl13a was ranked the highest by BestKeeper and geNorm, but second to lowest by NormFinder and DeltaCt. These discrepancies can be explained by the algorithmic differences and ranking criteria used by each program. For example, geNorm prioritizes the pairwise variation of a particular gene with all the other candidate genes, whereas NormFinder uses a model-based approach to estimate the expression variation of each gene independently of the other genes [56, 57]. These program-specific differences further emphasize the utility of RefFinder. When both the inter- and intra-group variances were considered in NormFinder, Rpl13a was ranked the least stable, consistent with our data showing significant differences in the mean Ct values between E15 and P0. The age-dependent differences in Rpl13a expression suggest that it is developmentally regulated and thus not an appropriate reference for target gene normalization in cortical development studies. Actb was consistently ranked the lowest in all algorithms and did not meet, albeit barely, the cutoff value for stable genes by BestKeeper criteria. Although Actb stability values were within the range for stable genes using the other algorithms, we suggest using alternate genes that are more stably expressed. Taking into consideration the results from multiple stability ranking algorithms, we propose using B2m, Gapdh, and Hprt, or a combination of B2m/Gapdh or B2m/Hprt, as reference genes for RT-qPCR in mouse cortical samples spanning late corticogenesis.

Our study re-emphasizes the importance of validating reference genes in both the specific tissues and developmental stages of interest. In a previous developmental study using whole brain tissue from earlier mouse embryonic stages (E11 to E15), Actb was ranked the third most stable gene out of ten evaluated genes, whereas Gapdh was in sixth place [45]. This is in contrast to our findings in E15 to P0 cortex-specific samples where Gapdh was among the most stable gene and Actb was the least stable. Studies across different developmental windows in other brain regions and nervous system tissues have also reported different stability rankings for the genes we assessed [47]. To our knowledge, only one previous study has examined the stability of reference genes specifically in mouse cortical tissue during embryonic development by RT-qPCR [44]. In this study, the authors used various descriptive statistics to examine the stability of Actb, Gapdh, and Hprt, as well as ribosomal protein 18s (18s) and RNA polymerase II (RpII), across nine developmental stages between E10 and P56 in C57BL/6 mice. In their sample set, Gapdh was consistently ranked the most stable gene, Hprt was ranked in the middle, and Actb was ranked the lowest. In the present study, we applied several computational algorithms to analyze reference gene expression stabilities in the period of late corticogenesis (E15-P0) in CD-1 mice. Our results with regards to Gapdh, Hprt, and Actb stabilities corroborate the former study, highlighting a consistency in the results across mouse strain backgrounds. Additionally, we identified B2m as a reliable reference gene for late corticogenesis studies. This provides more options for reference gene selection, considering optimal references should have Ct values as close as possible to that of the target gene to ensure accurate quantification [62].

The most stable gene in our data set, B2m, encodes for β2 microglobulin, a subunit of the major histocompatibility complex class I (MHC-I) [63]. Recent studies have shown that β2 microglobulin plays crucial roles in several neurological disorders, however, the role in cortical development is not well understood [63]. The second most stable genes, Gapdh and Hprt, encode enzymes that participate in crucial metabolic pathways, including glycolysis and purine salvaging, respectively [64, 65]. Our findings suggest that these genes are consistently expressed to support these vital processes during late corticogenesis. The lowest-ranked gene, Actb, encodes for β-actin, which is a structural protein that is an essential component of the cytoskeleton [66]. The period between E15 and P0 reflects a time window when substantial cell migration events and morphogenesis are occurring. These processes require dynamic cytoskeletal changes, which may explain the lowered Actb expression stability during this period. Unlike the other evaluated genes, Rpl13a displayed a small albeit significant decrease in expression between E15 and P0, suggesting its expression is developmental stage-dependent. Rpl13a encodes for a ribosomal subunit that is involved in the regulation of mRNA translation and protein synthesis [67]. The observed decrease in Rpl13a expression is consistent with studies demonstrating a developmental shift in the cortical ribosome signature between E15 and E18 that includes downregulation of multiple ribosomal proteins [68, 69]. These ribosome changes are thought to contribute to the dynamic gene regulatory mechanisms that drive the spatiotemporal development of the cortex, though the specific function of Rpl13a in cortical development is unknown.

Finally, there are several limitations to our study. Here, we only tested five genes for their reliability as references in cortical tissue from E15 to P0, and other yet-to-be-evaluated housekeeping genes may also be suitable for this purpose. Furthermore, we did not assess the impact of sex on gene stability, but this should be considered for sex-dimorphic development studies since it has been reported that housekeeping gene stability can vary between males and females during brain development [45]. Lastly, our study analyzed total RNA from whole cortical lysates and does not account for cell type-specific gene expression. Given the numerous cell types that are present during the final stages of corticogenesis (e.g., pyramidal neurons, interneurons, and glia), single-cell transcriptomic studies during mouse cortical development, such as those by Loo et al. [70] and Di Bella et al. [71], will be useful to understand gene expression in single cell types.

Conclusions

In conclusion, our result supports the stable expression of B2m, Gapdh, and Hprt in the mouse cortex during the period of E15 to P0, and we recommend using B2m, Gapdh, and Hprt or a combination of B2m/Gapdh and B2m/Hprt as reliable RT-qPCR reference genes for studying mouse late corticogenesis. We caution against using Actb due to lower expression stability and do not recommend using Rpl13a since its expression changes with developmental stage. The differences in expression stabilities of known housekeeping genes during cortical development may reflect their distinct roles in developmental processes and further underscore the importance of validating the expression stability of reference genes for the experimental tissue and conditions of interest.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

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  1. Ananya Uppalapati and Timothy Wang contributed equally to this work.

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  1. Department of Neuroscience, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, 75080, USA

    Ananya Uppalapati, Timothy Wang & Lena H. Nguyen

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AU and TW collected and analyzed the data. LN designed the study, analyzed the data, and wrote the manuscript. All authors read and approved the final manuscript.

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Uppalapati, A., Wang, T. & Nguyen, L.H. Evaluation of suitable reference genes for gene expression studies in the developing mouse cortex using RT-qPCR. BMC Neurosci 26, 12 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12868-025-00934-y

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