Circadian Rhythms in a Petri Dish: Synchronizing Mouse Hypothalamic mHypoE-42 Cells for Circadian Clock Gene Expression Analysis

Summary

This protocol provides a practical and reproducible workflow for synchronizing cultured cells, performing staggered time-course sampling over 24–72 h without overnight collection, and analyzing circadian clock gene expression.

Abstract

Circadian rhythms are endogenous oscillations of approximately 24 h that regulate a wide range of cellular and physiological processes, including gene expression, metabolism, and behavior. These rhythms arise from interconnected transcriptional–translational feedback loops that respond to temporal and environmental cues. Because circadian regulation is highly dynamic, even minor experimental variations can influence phase, amplitude, and rhythmicity, making standardized experimental workflows essential for generating reliable and reproducible results. The goal of the present protocol is to provide a practical and reproducible workflow for synchronizing cultured cells, performing time-course sampling, and analyzing circadian clock gene expression under standard laboratory conditions. The protocol describes serum shock-based synchronization, staggered sample collection over 24–72 h to avoid overnight sampling, ribonucleic acid extraction, complementary deoxyribonucleic acid synthesis, quantitative real-time polymerase chain reaction, and circadian rhythm analysis using appropriate statistical approaches. The workflow also highlights critical experimental considerations, including synchronization conditions, sample quality assessment, reference gene selection, and data analysis, to improve reproducibility across experiments. This method provides an accessible approach for investigating molecular circadian mechanisms and evaluating rhythmic gene expression in cultured cells, facilitating studies of circadian regulation in physiological and disease-related experimental models.

Introduction

Circadian rhythms are endogenous cycles with a period of approximately 24 h that regulate a wide range of physiological, metabolic, and cellular processes¹. In mammalian systems, these rhythms are coordinated by a central clock located in the suprachiasmatic nucleus (SCN) of the hypothalamus and by self-sustained molecular clocks at the cellular level. These clocks consist of interlocked transcriptional–translational feedback loops involving core clock genes, including circadian locomotor output cycles protein kaput (Clock), basic helix-loop-helix aryl hydrocarbon receptor nuclear translocase-like 1 (Bmal1), period (Per), and cryptochrome (Cry)². Given the central role of the circadian clock in diverse biological processes, in vitro cell models have become important tools for investigating the molecular mechanisms of temporal regulation under controlled experimental conditions.

The goal of the present protocol is to provide a reliable, practical, and reproducible workflow for synchronizing cultured cells using a serum shock protocol, performing staggered time-course sampling, and analyzing circadian clock gene expression while avoiding overnight sample collection. By improving the feasibility of circadian sampling without compromising data quality, this workflow is intended to facilitate studies of rhythmic biological processes using standard laboratory techniques.

Circadian studies have been performed in a wide range of in vitro models, including human cancer cell lines³, rat-1 fibroblasts⁴, SCN explants⁵, and organoids⁶. Although most mammalian cells possess functional circadian clocks, the absence of synchronizing cues, such as light, rapidly leads to desynchronization in culture, limiting the study of circadian rhythms in vitro⁷. Therefore, synchronization techniques are essential for investigating molecular clock mechanisms and their implications in health and disease.

Several synchronization strategies have been developed for cultured mammalian cells. Glucocorticoid-based synchronization using dexamethasone is among the most widely used approaches because it robustly resets circadian rhythms in many cell types while reducing some of the variability associated with serum stimulation⁸. Similarly, forskolin-mediated activation of cyclic adenosine monophosphate signaling has been successfully used to synchronize peripheral clocks in selected cellular models⁹. Other approaches include treatment with blood-borne signals⁴, different chemical compounds^8,10, medium exchange³, temperature shock¹¹, oxygen cycles¹², and mechanical stimulation¹³. Collectively, these methods have substantially advanced circadian research; however, they may require specialized equipment, specific experimental conditions, or additional optimization, making it important to validate the selected synchronization strategy for each biological model and experimental application¹⁴.

Serum shock is one of the most widely established synchronization approaches and consists of exposing cultured cells to a short-term, high-serum stimulus⁴. Because the stimulus is transient, prolonged perturbation of cellular signaling pathways is minimized⁴, reducing potential confounding effects on downstream analyses. Consequently, serum shock is particularly suitable for studies of circadian gene expression and molecular clock function. In addition, the procedure is straightforward to implement and does not require specialized equipment, making it accessible and readily reproducible across laboratories. A practical limitation of serum shock experiments, however, is the intensive sampling schedule required to characterize circadian rhythmicity accurately. Reliable rhythm analysis typically requires sample collection at regular intervals, usually every 2–6 h over one or more circadian cycles (24–72 h)¹⁴, often necessitating overnight collections.

The workflow described here addresses this logistical challenge by staggering the initiation of synchronization across multiple culture plates, thereby enabling complete circadian sampling during standard working hours while avoiding overnight sample collection. Rather than introducing a new synchronization strategy, this protocol provides a detailed and reproducible implementation of an established serum shock method combined with a practical sampling schedule and downstream gene expression analysis. This workflow is particularly appropriate for studies of circadian gene expression in cultured cells using widely available laboratory techniques. When adapting the protocol to additional cellular models, experimental parameters, including cell density, synchronization conditions, sampling intervals, and reference gene selection, should be optimized and validated for the specific cell type before implementation.

Protocol

The following protocol describes a validated synchronization method for the mHypoE-42 embryonic mouse hypothalamic cell line. Validate and optimize the synchronization protocol independently before applying it to other cultured cell types. See Figure 1 for an overview of the workflow and Supplementary Figure 1 for the weekly sampling schedule. Refer to the Table of Materials for all reagents, equipment, and instruments used in this protocol. Perform all cell culture procedures in a certified biosafety cabinet using sterile reagents and equipment.

Figure 1. Schematic overview of the staggered serum shock synchronization protocol. Schematic illustrating the workflow used to synchronize mHypoE-42 cells by serum shock in two staggered experimental sets. The staggered design enables circadian sample collection during standard working hours while avoiding overnight sampling. Samples are subsequently processed for RNA extraction, quantitative real-time polymerase chain reaction (qRT-PCR), and circadian rhythm analysis. Created with Biorender.com. Please click here to view a larger version of this figure.

1. Cell culture

1. Culture adherent mHypoE-42 cells in Dulbecco’s Modified Eagle Medium (DMEM), high glucose (4500 mg/L), supplemented with 10% (v/v) heat-inactivated fetal bovine serum (FBS), 3.7 g/L sodium bicarbonate, and 1% antibiotic solution.
2. Maintain the cells at 37°C in a humidified incubator containing 95% air and 5% carbon dioxide (CO₂) using T75 culture flasks with a final culture volume of 10 mL.
   NOTE: Test each new batch of FBS before beginning experiments because serum batches may vary.
3. Subculture the cells when they reach approximately 80%–90% confluence. Wash the cells with sterile 1× phosphate-buffered saline (PBS; pH 7.4), add 3 mL of 0.5% trypsin solution, and incubate for 3–5 min to detach the cells.
4. Add at least twice the trypsin volume of complete culture medium containing 10% (v/v) FBS to neutralize the trypsin.
5. Dilute the cells with fresh growth medium at a ratio of up to 1:2.
   NOTE: Avoid using cells that have undergone excessive passaging (e.g., passages greater than 40 for mHypoE-42 cells). Prewarm all solutions before use. Regularly test cultures for mycoplasma contamination.
6. Proceed with synchronization assays 24 h after seeding the cells at 2.0 × 10⁵ cells per well, as described in Step 2. Maintain routine cell cultures at approximately 80%–90% confluence before passaging.

2. Synchronization of mHypoE-42 Cell Line

1. Cell preparation
   1. After trypsinization, centrifuge the cells at 700 × g for 5 min.
   2. Discard the supernatant and resuspend the cell pellet in 10 mL of fresh culture medium.
   3. Mix the cell suspension with trypan blue dye at a 1:1 ratio (20 μL total volume) and determine the cell concentration using a hemocytometer.
      NOTE: Dispose of trypan blue-containing solutions according to institutional chemical waste disposal guidelines. Do not discard unused dye into the sink.
2. Cell plating and staggered experimental design
   1. Plate 2.0 × 10⁵ cells per well in either 6-well plates or 35-mm culture dishes. Incubate the cultures at 37°C for 24 h.
      NOTE: Initiate serum synchronization 24 h after cell seeding. Therefore, a predefined cell confluence is not required before synchronization. Instead, examine the cultures microscopically to confirm that the cells are fully attached and display appropriate morphology before beginning the synchronization procedure.
   2. Divide the culture plates randomly into two experimental sets. Use the first set to generate circadian timepoints (CT) CT0, CT4, CT8, CT24, CT28, and CT32. Use the second set to generate CT12, CT16, CT20, CT36, CT40, CT44, and CT48 (Figure 1; Supplementary Figure 1).
   3. Increase the number of wells or plates as needed to include technical replicates.
      NOTE: Minimize variability by preparing all plates from the same cell suspension, maintaining identical seeding densities and culture conditions, and performing synchronization using the same reagents and experimental settings. Adjust culture vessel format and plating density as needed for other cell types. When using 6-well plates, combine CTs collected simultaneously to optimize plate usage.
3. Synchronization
   1. After 24 h, aspirate the medium from the first set of plates and add 1 mL of serum-free DMEM. Incubate for 12 h to induce serum starvation.
   2. Prepare a 50% (v/v) horse serum solution in high-glucose DMEM immediately before use.
   3. Aspirate the starvation medium from the first set and expose the cells to 1 mL of the freshly prepared 50% horse serum solution for 2 h.
   4. Simultaneously aspirate the medium from the second set of plates and replace it with 1 mL of serum-free DMEM. Incubate for 12 h.
   5. After the 2-h serum shock, aspirate the medium and add 1 mL of high-glucose DMEM supplemented with 10% (v/v) heat-inactivated FBS. Designate this time point as CT0.
4. Sample collection
   1. Place the culture plates on ice. Aspirate the medium, wash the cells twice with 1 mL of cold sterile 1× PBS, discard the wash solution, and lyse the cells mechanically using up to 1 mL of lysis reagent.
      CAUTION: The lysis reagent contains hazardous chemicals. Perform all handling steps in a certified chemical fume hood while wearing appropriate personal protective equipment. Dispose of waste according to institutional chemical safety guidelines.
   2. Transfer each lysate to a sterile tube and store the samples at −80°C until RNA extraction.
   3. Repeat Steps 2.4.1 and 2.4.2 to collect samples at CT4 and CT8.
   4. At CT8, repeat Step 2.3.2 for the second experimental set to synchronize the other cells.
   5. After 2 h of serum shock, terminate synchronization of the second experimental set by replacing the medium with 1 mL of high-glucose DMEM supplemented with 10% (v/v) heat-inactivated FBS.
   6. Collect samples alternately from the two experimental sets to obtain CT12, CT24, CT16, CT28, CT20, and CT32. On the following day, repeat the collection procedure to obtain CT36, CT40, CT44, and CT48.
      NOTE: Serum starvation and synchronization were initiated according to the experimental timeline after cell seeding rather than at a predefined cell confluence. Confirm that cells are fully attached and exhibit appropriate morphology before beginning synchronization. Although consecutive CTs differ by 4 h, collections are not performed at 4-h intervals. Refer to Supplementary Figure 1 for the complete weekly collection schedule.

3. Sample Processing: RNA Extraction

NOTE: Protocols may vary depending on the experimental context and application. The following workflow describes an example of an effective procedure for RNA extraction, complementary DNA (cDNA) synthesis, and quantitative real-time polymerase chain reaction (qRT-PCR). Extract total RNA using a combined phenol-based lysis and phase separation method followed by purification with silica spin columns.

1. Sample preparation and phase separation
   1. Thaw the samples, re-homogenize the lysates, and incubate them at room temperature for 5 min.
   2. Add 200 μL of chloroform for every 1 mL of lysis reagent.
      CAUTION: Chloroform is volatile and toxic. Perform all procedures involving chloroform in a certified chemical fume hood while wearing appropriate personal protective equipment.
   3. Shake the tubes vigorously for 15 s and incubate them at room temperature for 3 min.
   4. Centrifuge the samples at 12,000 × g for 15 min at 4°C. Allow the samples to separate into three phases: a lower phenol-chloroform phase containing proteins, a white interphase containing DNA, and an upper aqueous phase enriched in RNA.
   5. Carefully transfer the aqueous phase to a new sterile tube without disturbing the interphase. Store the organic phase at 4°C if subsequent protein or DNA isolation is planned.
      NOTE: Perform protein or DNA isolation according to the appropriate protocol if these biomolecules will be recovered from the organic phase.
2. RNA purification
   1. Add an equal volume of 70% ethanol (prepared with RNase-free water) to the aqueous phase and vortex twice for 15 s to promote RNA binding.
   2. Load the sample onto a silica spin column and purify the RNA according to the column purification workflow.
   3. Digest contaminating genomic DNA with DNase for 15 min at room temperature.
   4. Wash the column using the supplied wash buffers.
   5. Perform an additional wash with 100 μL of wash buffer and centrifuge at 11,000 × g for 2 min to dry the silica membrane.
   6. Transfer the spin column to a clean microcentrifuge tube. Elute the RNA with 40–60 μL of RNase-free water by applying the water directly to the center of the silica membrane and centrifuging at 11,000 × g for 1 min.
      NOTE: Select the elution volume according to the desired RNA yield and concentration. An elution volume of 60 μL generally provides more complete RNA recovery from the silica membrane but produces a more dilute RNA solution. In contrast, an elution volume of 40 μL yields a more concentrated RNA preparation but may reduce total RNA recovery. Optimize the elution volume according to the manufacturer’s recommendations and the requirements of the downstream application.
3. RNA quality assessment
   1. Place the RNA samples on ice and transfer 5 μL of each sample to a new sterile microcentrifuge tube for quality assessment.
   2. Evaluate RNA integrity using either 1.5% agarose gel electrophoresis or capillary electrophoresis. Confirm the presence of distinct 28S and 18S ribosomal RNA bands.
   3. Determine RNA concentration and purity by measuring absorbance with a spectrophotometer.
      NOTE: Dispose of phenol-containing waste according to institutional chemical waste disposal guidelines. Do not dispose of hazardous chemical waste in the sink. For cultured cell samples, RNA concentrations typically range between 200 and 1000 ng/μL and are suitable for cDNA synthesis and qRT-PCR. Accept RNA samples with an A₂₆₀/₂₃₀ ratio between 2.0 and 2.2 and an A₂₆₀/₂₈₀ ratio of approximately 2.0 (see Supplementary Table 1).
4. RNA storage
   1. Store purified RNA samples at −80°C until cDNA synthesis.

4. Sample Processing: cDNA Synthesis

NOTE: Synthesize cDNA from 1 μg of total RNA using a first-strand cDNA synthesis kit. Adjust reaction volumes and incubation conditions according to the manufacturer’s instructions.

1. Reverse transcription
   1. Thaw the RNA samples on ice. Prepare a 20 μL reverse transcription reaction containing 1 μg of total RNA, 10 μL of 2× Master Mix (containing ribonuclease inhibitor, random hexamers, oligo(deoxythymidine) primers, magnesium ions [Mg²⁺], and deoxynucleotide triphosphates), 2 μL of enzyme mix (containing reverse transcriptase), and diethyl pyrocarbonate-treated water to a final volume of 20 μL.
   2. Incubate the reaction in a thermal cycler for 10 min at 25°C to anneal the primers, followed by 30 min at 50°C for cDNA synthesis. Inactivate the reverse transcriptase by incubating the reaction at 85°C for 5 min.
   3. Add 1 μL of RNase H and incubate the reaction for 20 min at 37°C to degrade the RNA strand of the RNA–DNA hybrid.
   4. Dilute the cDNA preparation up to 1:20 with sterile water, according to assay optimization requirements, and store the diluted cDNA at −20°C until quantitative real-time polymerase chain reaction (qRT-PCR) analysis.

5. Sample Processing: Real-Time Quantitative Polymerase Chain Reaction (qRT-PCR)

NOTE: Measure the expression of circadian clock genes using qRT-PCR. Refer to Supplementary Table 2 for validated primer sequences, annealing temperatures, and primer concentrations.

1. Assay optimization
   1. Validate and optimize the primer concentration, annealing temperature, and cDNA input using pooled cDNA samples and a standard curve before analyzing experimental samples. Perform assay optimization and validation as previously described^15,16.
2. Reaction setup
   1. Prepare each 10 μL qRT-PCR reaction by combining 5 μL of 2× master mix, 0.4 μL each of the forward and reverse primers, 0.2 μL of diethyl pyrocarbonate-treated water, and 4 μL of cDNA template in a 96-well PCR plate.
   2. Seal the plate with an optically clear sealing film or sealing mat. Centrifuge the plate at 700 × g for up to 5 min at 4°C to remove bubbles and collect the reaction mixture at the bottom of each well.
   3. Analyze all cDNA samples to determine the expression of the target genes Bmal1 and Per2.
   4. Analyze the selected reference gene (B2m) using the same cDNA dilution to normalize gene expression across samples.
3. Thermal cycling
   1. Perform qRT-PCR using the following cycling conditions: initial denaturation and activation of the hot-start DNA polymerase at 95°C for 2 min, followed by 39 cycles of 95°C for 5 s and 60°C for 30 s.
   2. Perform melt curve analysis by heating the reactions to 95°C for 5 s, followed by incremental increases of 0.5°C to 95°C, holding for 5 s at each increment.
      NOTE: Adjust hold times and melt curve settings according to the master mix recommendations when required.
4. Reference gene validation
   NOTE: In serum synchronization experiments, evaluate candidate reference genes because serum treatment may alter housekeeping gene expression¹⁷. Assess expression stability using methods such as BestKeeper¹⁸, geNorm¹⁹, NormFinder²⁰, or the comparative ΔCt method²¹. Select the most stable gene(s) for normalization. In this study, β-actin (Actb) and β-2-microglobulin (B2m) were evaluated using RefFinder software²², which integrates multiple reference gene validation algorithms. B2m exhibited the greatest expression stability across the circadian time course and was therefore selected for normalization.
5. Product verification
   1. Verify amplification specificity by analyzing the PCR products using 2% agarose gel electrophoresis.
      NOTE: Determine the optimal annealing temperature by testing multiple temperatures (e.g., 58 °C, 60°C, and 62°C). Select the temperature that produces the lowest quantification cycle (Cq) value while maintaining a single melt curve peak without evidence of primer-dimer formation.
      NOTE: Accept assays with amplification efficiencies between 90% and 110% and a coefficient of determination (R²) of 0.99 or greater. Confirm that melt temperatures differ by no more than 0.5°C between technical replicates. Accept only technical replicates with a Cq difference of ≤0.5 for analysis and use these values to calculate mean expression levels. Perform all reactions in technical replicates (e.g., triplicates) and include both a non-template control and a no-reverse-transcription control.

6. Data Analysis

1. Gene expression analysis
   1. Average the technical replicates for each sample before downstream analysis.
   2. Calculate the ΔCq value for each sample and time point using the following equation:
      ΔCq   = Cq_{target gene}  -  Cq_{housekeeping gene}
   3. Calculate the ΔΔCq value for each sample and time point using the following equation:
      ΔΔCq   = ΔCq_sample  -  ΔCq_control  
      NOTE: For the calculation of ΔΔCq, ΔCq_control was defined as the mean ΔCq value obtained by averaging all control samples across all CTs.
   4. Evaluate the circadian parameters, including the midline estimating statistic of rhythm (MESOR), amplitude, peak, and phase, using CircaCompare²³. Perform the analysis using either the online application or the R package implementation.
2. Online CircaCompare analysis
   NOTE: The online implementation reports complete circadian parameter estimates only when the dataset satisfies the statistical criteria for rhythmicity. When rhythmicity is weak or not statistically significant, use the R implementation to obtain parameter estimates for all datasets.
   1. Prepare a comma-separated values (.csv) file.
      1. Enter the time variable (numeric; hours) in Column 1.
      2. Enter the grouping variable (two experimental groups only) in Column 2.
      3. Enter the outcome variable (numeric; ΔΔCq) in Column 3.
   2. Upload the .csv file to the online CircaCompare application.
   3. Assign the time, group, and outcome variables to the corresponding columns.
   4. Select Run to perform the analysis.
3. CircaCompare analysis in R
   1. Perform the analysis using R version 4.5.1 and CircaCompare package version 0.2.0²⁴.
   2. Install the required R packages.
      1. Install the devtools package (version 2.5.2).
      2. Install the circacompare package (version 0.2.0).
      3. Install the ggplot2 package (version 4.0.3).
      4. Install the svglite package (version 2.2.2).
   3. Upload the input .csv file.
   4. Execute the CircaCompare analysis after setting alpha_threshold = 1.
      NOTE: Setting alpha_threshold = 1 reports circadian parameter estimates for all fitted oscillations regardless of statistical significance. However, only oscillations with a p-value < 0.05 should be interpreted as rhythmic and statistically significant.
   5. Customize Supplementary File 1 before executing the script.
      1. Replace “XXX.csv” with the input data file name.
      2. Replace “YYY.csv” with the desired results file name.
      3. Replace “ZZZ.svg” with the desired plot file name.
      4. Adjust the y-axis limits (ylim) and any additional plotting parameters according to the dataset.
   6. Refer to Supplementary Table 3 for an example input file.

Results

Successful synchronization is indicated by statistically significant circadian rhythmicity of the analyzed clock genes under the control condition (p < 0.05). In this study, three independent biological experiments were performed, with 0.1% dimethyl sulfoxide serving as the negative control. Bmal1 and Per2 were selected as representative circadian markers because they are core components of the molecular circadian clock and exhibit a well-characterized antiphasic expression pattern. Representative rhythmic expression profiles obtained following synchronization are shown in Figure 2. The corresponding circadian parameters, including rhythmicity, MESOR, amplitude, and peak time, are summarized in Table 1, while the pairwise comparison between Bmal1 and Per2 is presented in Table 2.

Figure 2. Circadian oscillations of Bmal1 and Per2 following serum shock synchronization. Representative circadian expression profiles of Bmal1 (dark blue) and Per2 (light blue) in synchronized mHypoE-42 cells measured by quantitative real-time polymerase chain reaction (qRT-PCR) from 24 to 48 h after synchronization. Curves represent the fitted circadian models generated using CircaCompare.Please click here to view a larger version of this figure.

Gene	Rhythmicity (p-value)	MESOR	Amplitude	Peak time (h)
Bmal1	0.0205	−0.2948	0.4648	20.4239
Per2	0.0071	−0.318	0.4629	9.4883
Bmal1 + Compound A	0.1068	—	—	—

Table 1: Circadian rhythm parameters estimated for Bmal1 and Per2 under basal conditions and for Bmal1 following Compound A treatment. Circadian rhythmicity was evaluated using CircaCompare. The table reports the p-value for rhythmicity, the midline estimating statistic of rhythm (MESOR), amplitude, and peak time for each condition. Circadian parameters are not reported for Bmal1 following Compound A treatment because rhythmicity was not statistically significant (p = 0.1068).

MESOR difference estimate (p-value)	Amplitude difference estimate (p-value)	Phase difference estimate (p-value)	Shared period (h)
−0.0232 (p = 0.8904)	−0.0019 (p = 0.9936)	−10.9356 (p = 2.3155 × 10⁻6)	24

Table 2: Pairwise comparison of circadian parameters between Bmal1 and Per2. Pairwise comparison of the midline estimating statistic of rhythm (MESOR), amplitude, and phase between the fitted circadian expression profiles of Bmal1 and Per2, as estimated using CircaCompare. Values are presented as parameter differences with the corresponding p-values. The shared period was fixed at 24 h during model fitting.

Under basal conditions, both Bmal1 and Per2 exhibited statistically significant rhythmicity (Bmal1: p = 0.0205; Per2: p = 0.0071). CircaCompare analysis further demonstrated that the two genes oscillated in opposite phases, with a phase difference of −10.9356 h (p = 2.3155 × 10⁻⁶), consistent with their expected antiphasic relationship within the molecular circadian clock network. The RNA quality metrics supporting downstream gene expression analysis are provided in Supplementary Table 1.

The protocol can also be applied to compare circadian rhythmicity between experimental conditions, including healthy versus disease models or control versus treatment groups. As a representative example, an emerging environmental contaminant and endocrine disruptor previously detected in the human hypothalamus was evaluated and is referred to here as Compound A. Representative results are shown in Figure 3, and the corresponding rhythmicity analysis is summarized in Table 1.

Figure 3. Effect of Compound A on the circadian rhythmicity of Bmal1 expression. Representative Bmal1 expression profile in synchronized mHypoE-42 cells following treatment with Compound A, measured by quantitative real-time polymerase chain reaction (qRT-PCR). Curves represent the fitted circadian models generated using CircaCompare. Please click here to view a larger version of this figure.

Following exposure to Compound A, Bmal1 no longer exhibited statistically significant rhythmicity (p = 0.1068). Under these conditions, the fitted circadian model did not satisfy the statistical criteria for rhythmicity. Consequently, circadian parameters such as MESOR, amplitude, and peak time are not reported because they are not considered biologically interpretable when rhythmicity is not statistically significant. This example illustrates how the workflow can distinguish rhythmic from non-rhythmic expression profiles and can be used to assess potential alterations in circadian gene expression under different experimental conditions. To minimize potential technical variability associated with the staggered sampling strategy, all culture plates were prepared from the same cell suspension, maintained under identical culture conditions, and synchronized using the same experimental workflow, differing only in the timing of synchronization.

Supplementary Figure 1. Weekly schedule of the staggered synchronization protocol. Representative weekly timeline illustrating cell plating, serum starvation, serum shock synchronization, synchronization arrest, and circadian sample collection for the two staggered experimental sets (Set 1, purple; Set 2, green). The staggered design enables collection of circadian timepoints (CT) CT0–CT48 during standard working hours while avoiding overnight sample collection. Please click here to download this file.

Supplementary Table 1. RNA quality metrics for all biological samples included in the circadian time-course experiment. RNA purity was assessed using the absorbance ratios A₂₆₀/A₂₈₀ and A₂₆₀/A₂₃₀, and RNA concentration was determined spectrophotometrically before complementary DNA (cDNA) synthesis. Samples are identified by collection time, treatment group, and biological replicate. These measurements were used to verify RNA quality prior to downstream quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Please click here to download this file.

Supplementary Table 2. Primer sequences and amplification conditions used for quantitative real-time polymerase chain reaction (qRT-PCR). Forward and reverse oligonucleotide primer sequences are presented in the 5′-3′ orientation together with the corresponding NCBI accession number(s), annealing temperature, and primer concentration used for amplification of each target gene. Please click here to download this file.

Supplementary Table 3. Example input dataset for circadian rhythm analysis using CircaCompare. Example comma-separated values (CSV) dataset formatted for analysis with CircaCompare. The dataset contains three required variables: Time (h), Group, and Outcome (ΔΔCq), corresponding to the format described in the Data Analysis protocol and used as the example input file for circadian parameter estimation. Please click here to download this file.

Supplementary File 1. Example R script for circadian rhythm analysis using CircaCompare. Example R script for analyzing circadian gene expression data using the CircaCompare package (version 0.2.0) in R (version 4.5.1). The script imports the input dataset, performs circadian parameter estimation with a 24-h period, exports the statistical summary, and generates a publication-quality plot. Before use, replace the placeholder file names (XXX.csv, YYY.csv, and ZZZ.svg) with the desired input and output file names and adjust the plotting parameters (e.g., ylim) as appropriate for the dataset. Please click here to download this file.

Discussion

Circadian rhythms are endogenous cycles of approximately 24 h that regulate a wide range of physiological, metabolic, and cellular processes through conserved molecular clock mechanisms^1,2. In vitro synchronization models provide a controlled experimental environment for investigating these mechanisms and have become valuable tools for studying circadian regulation under normal and pathological conditions, including exposure to environmental contaminants and other experimental perturbations²⁵. The protocol presented here describes a practical workflow for synchronizing cultured cells by serum shock, collecting time-course samples using a staggered sampling strategy, and quantifying circadian gene expression by qRT-PCR. This workflow is particularly suitable for studies requiring temporal gene expression profiling while minimizing the logistical challenges associated with overnight sample collection. Several critical steps determine the success and reproducibility of this protocol. Appropriate control of cell confluency, serum starvation, serum shock duration, and synchronization conditions is essential because inadequate synchronization may result in weak or undetectable rhythmicity¹⁴. Consistent cell seeding density, preparation of all experimental plates from the same cell suspension, and identical culture conditions across the staggered experimental sets further reduce technical variability. RNA quality, cDNA synthesis, primer validation, and selection of stable reference genes are also important determinants of reliable qRT-PCR results. As with any circadian synchronization protocol, optimization of these parameters may be required when adapting the workflow to different cell types or experimental conditions.

Although serum shock is one of the most widely established synchronization methods in circadian biology^4,26, it is not universally optimal for every experimental application. Alternative synchronization approaches, including dexamethasone treatment, forskolin stimulation, temperature entrainment, and reporter-based systems, may be more appropriate depending on the biological model and research objective¹⁴. These methods should be considered complementary rather than competing approaches, as each possesses distinct advantages and limitations. Serum shock provides a practical balance between experimental simplicity, accessibility, and reproducibility without requiring specialized instrumentation or genetically encoded reporter systems. Nevertheless, investigators should carefully consider the potential influence of the synchronization stimulus on their biological system and select the approach that best addresses their experimental question. Serum shock protocols that omit the serum starvation step have also been reported and may represent suitable alternatives in specific experimental contexts²⁷. The flexibility of this protocol allows several modifications and troubleshooting strategies. The staggered sampling design relies on parallel culture plates synchronized at different times to generate complementary CTs while avoiding overnight sample collection. To minimize inter-plate variability, prepare all plates from the same cell suspension, seed them at identical densities, maintain identical culture conditions, and perform synchronization using the same experimental workflow, differing only in the timing of synchronization. Although this approach cannot completely eliminate batch-related variation, it minimizes technical variability while substantially reducing the logistical challenges associated with overnight sampling and preserving the ability to detect biologically meaningful circadian rhythms. Although the staggered sampling strategy was designed to minimize technical variability by maintaining identical culture conditions across all experimental sets, it was not directly compared with a conventional continuous sampling design in the present study. Future studies may further evaluate the equivalence of these approaches in additional experimental models. When adapting this workflow to new cell types or experimental conditions, optimize synchronization efficiency and assess potential batch effects during protocol validation. Depending on the experimental objective, modify the synchronization schedule by adjusting synchronization start times or by maintaining a single synchronized culture with a reduced sampling window. Troubleshooting strategies include optimizing serum shock duration, verifying cell viability and confluency before synchronization, confirming RNA quality prior to downstream analysis, and adjusting sampling intervals to improve circadian rhythm detection. The present protocol was validated in the mHypoE-42 embryonic mouse hypothalamic cell line and therefore should be independently optimized and validated before application to other cellular models. Although not evaluated in the present study, this protocol was developed based on previous optimization in our laboratory using other cell lines (e.g., N2a cells and human fibroblasts), supporting its potential applicability to different cellular models. Likewise, the staggered sampling strategy substantially improves experimental feasibility by enabling complete circadian sampling during standard working hours; however, users should maintain identical experimental conditions across all culture plates to minimize potential batch effects. When appropriately optimized, this workflow provides an accessible and reproducible approach for investigating circadian gene expression and evaluating experimental interventions that alter molecular clock function across a broad range of research applications.

Despite its practical advantages, this method has several limitations. The staggered sampling strategy provides discrete time-point measurements rather than continuous monitoring, which reduces temporal resolution compared with fluorescence- or bioluminescence-based reporter assays⁷. Consequently, the accuracy of period estimation and the detection of subtle phase shifts or low-amplitude oscillations may be reduced. In addition, the plate-based sampling workflow becomes increasingly labor-intensive as the number of treatments or experimental conditions increases because additional culture plates are required for each condition. Among existing methodologies, this workflow provides a practical alternative to reporter-based systems, including E-box-driven luciferase assays (e.g., Bmal1-Luc and Per2-Luc), which enable real-time monitoring of circadian activity in living cells^28,29. Although fluorescence- and bioluminescence-based reporter systems provide high temporal resolution and are particularly useful for high-throughput screening of candidate circadian modulators, they require stable genetic modification, specialized detection equipment, and tightly controlled environmental conditions³⁰, which may not be available in all laboratories. Furthermore, reporter systems are generally restricted to predefined reporter constructs and may require normalization to constitutive signals or cellular abundance, limiting simultaneous assessment of multiple genes or downstream molecular analyses. In contrast, the workflow described here is compatible with multiple downstream applications, including quantitative gene expression analysis and protein-based assays, making it an accessible and versatile approach for laboratories investigating molecular circadian regulation.

Following sample collection and downstream analyses, appropriate statistical evaluation is essential to determine whether the observed oscillations are both statistically significant and biologically meaningful. In this protocol, CircaCompare provides a robust framework for estimating and statistically comparing the MESOR, amplitude, and phase using cosinor-based regression models²³. Unlike approaches that assess rhythmicity alone, CircaCompare enables direct pairwise comparisons of circadian parameters between experimental groups, facilitating evaluation of treatment effects, genetic perturbations, or disease-associated alterations in circadian behavior. This analytical workflow complements the synchronization protocol by providing standardized quantitative measures for interpreting changes in circadian gene expression. As demonstrated in the Representative Results, successful synchronization was indicated by the expected oscillatory expression patterns of Bmal1 and Per2 messenger RNA (mRNA) following serum shock (Figure 2). In contrast, exposure to Compound A resulted in the loss of statistically significant Bmal1 rhythmicity (Figure 3), illustrating the utility of this workflow for evaluating experimental interventions that alter circadian gene expression. An important consideration when interpreting circadian gene expression data is biological variability among independent experiments. In this study, the representative results were obtained from independent biological replicates performed on different days and across different cell passages. Consequently, some degree of variability is expected and reflects biological rather than technical variation. For this reason, synchronization experiments should include independent biological replicates and be interpreted within the context of the expected biological variability of the cellular model under investigation. Despite this variability, statistically significant rhythmicity and the expected antiphasic relationship between Bmal1 and Per2 were consistently observed. Furthermore, circadian characteristics, including oscillation amplitude, phase, period length, and damping rate, vary among cellular models. Therefore, synchronization performance should be evaluated within the context of the specific cell type under investigation rather than by direct comparison with unrelated models.

This workflow is particularly relevant for investigating the role of circadian rhythms in disease mechanisms and therapeutic responses. Disruption of circadian regulation has been implicated in numerous pathological conditions, including cancer, metabolic disorders, and neurodegenerative diseases^31,32. The ability to assess rhythmic gene expression or protein abundance in vitro provides a practical framework for investigating these processes and for evaluating the effects of pharmacological interventions on the molecular circadian clock, which is increasingly recognized as a potential therapeutic target^14,33. For example, this protocol can be applied to examine how candidate modulators influence circadian gene expression in disease-relevant cellular models following appropriate optimization and validation. Importantly, the purpose of the present work is not to advocate serum shock as a replacement for other synchronization methods or to suggest superiority over alternative approaches. Instead, this protocol provides a detailed and reproducible workflow for implementing a widely used synchronization strategy, collecting circadian samples during standard working hours using a staggered sampling design, and analyzing rhythmic gene expression with accessible downstream methodologies. We anticipate that this framework will be particularly useful for laboratories establishing circadian experiments or seeking a practical, reproducible, and accessible workflow for incorporating temporal analyses into existing cellular models.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
6-well cell culture plate	Nest Biotechnology	15140122	Cell culture plate
Agarose	NZYtech	MB02702	RNA quality assessment
Antibiotic mixture (penicillin/streptomycin)	Gibco, Thermo Fisher Scientific	D5648	Cell culture supplement
cDNA synthesis kit (first-strand)	NZYtech	MB12502	Reverse transcription
Chloroform	Sigma-Aldrich	A5256701	Phase separation reagent
DMEM, high glucose (4500 mg/L glucose, L-glutamine)	Sigma-Aldrich	26050088	Cell culture medium
Eppendorf microcentrifuge tubes	Eppendorf	30120086	Sample storage
Ethanol (96%)	Fisher Bioreagents	15552393	RNA purification
Fetal bovine serum, heat-inactivated	Gibco, Thermo Fisher Scientific	J62692.K7	Cell culture supplement
Hard-shell 96-well PCR plate	Bio-Rad Laboratories	HSP9601	qRT-PCR plate
Horse serum, heat-inactivated	Gibco, Thermo Fisher Scientific	15400054	Serum shock synchronization
mHypoE-42 cell line (CVCL_D443)	CELLutions Biosystems Inc.	MB13402	Embryonic mouse hypothalamic cell line
Oligonucleotide primers	NZYtech	MB12501	qRT-PCR primers
Phosphate-buffered saline (PBS)	Thermo Fisher Scientific	MB18502	Cell washing
qPCR Green Master Mix (2×)	NZYtech	MB22403	qRT-PCR reagent
Real-time PCR detection system	Bio-Rad Laboratories	EP0030108116	qRT-PCR instrument
RNA isolation kit	NZYtech	288306	Silica spin-column purification
RNA lysis reagent	NZYtech	MB18502	Phenol-based RNA extraction reagent
Sodium bicarbonate	Sigma-Aldrich	MB22401	Cell culture medium supplement
Sterile 35-mm culture dish	Thermo Fisher Scientific	121V	Cell culture dish
T100 thermal cycler	Bio-Rad Laboratories	1861096	cDNA synthesis
T75 tissue culture flask	Corning	430641U	Cell culture vessel
Trypan blue solution (0.4%)	Gibco, Thermo Fisher Scientific	15250061	Cell counting
Trypsin-EDTA (0.5%)	Gibco, Thermo Fisher Scientific	15400054	Cell dissociation