Data from: Biological sex differences in sleep of male and female c57bl/6 mice
Data files
Aug 30, 2024 version files 1.87 MB
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README.md
5.12 KB
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Sex_Differences_Sleep_Data.csv
1.87 MB
Abstract
Sleep is used as a physiological outcome in research, however, few studies have disaggregated sleep data based on sex. We investigated biological sex differences in sleep under normal physiological conditions using male and female C57BL/6 mice (n = 267 from 17 cohorts). Physiological parameters were recorded for 48-hours (2 days) using non-invasive piezoelectric cages to determine total sleep, non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wakefulness (WAKE). To investigate sleep differences between sexes we fit hierarchical generalized linear mixed models with nonlinear time effects. We found substantial sex differences in the sleep of C57BL/6 mice. Female mice slept less overall, specifically during the dark period, with less NREM sleep compared to males. Females also exhibited more REM sleep and WAKE. Females had shorter total and NREM sleep bout lengths compared to males. During the light period, REM bout lengths and WAKE bout lengths were similar between sexes; however, females had longer REM and WAKE bouts during the dark period. Variation in all outcomes was nominal between days and among cohorts, demonstrating rigor and replicability of our findings. Based on these substantial sex differences in sleep, failure to include both sexes in experimental designs or appropriately account for sex during analysis could lead to flawed inferences and inaccurate translational recommendations in pre-clinical sleep studies.
README: Data From: Biological Sex Differences in Sleep of Male and Female C57BL/6 Mice
https://doi.org/10.5061/dryad.76hdr7t39
Description of the data and file structure
Sex_Differences_Sleep.csv: File containing all sleep data from two continuous days under normal physiological conditions.
animal_ID – Unique identifier for individual mice.
Cohort – Categorical variable denoting the experimental cohort each mouse was in.
Sex – Biological sex of mouse
Timepoint – Sleep was measured for 2 consecutive days. Categorical variable where ‘A’ denotes the first 24 h and ‘B’ denotes the second 24 h.
Time – Categorical variable denoting the time of day (hour) that sleep-wake data were collected.
ZT – Categorical variable denoting zeitgeber time (ZT) where ZT0 was the time the lights in the housing room were turned on and ZT12 was lights off.
LD Cycle – Categorical variable denoting the light (L) or dark (D) cycle for each hour data were collected.
Percent_sleep – Continuous variable of the percent of each hour a mouse spent sleeping (%).
Percent_sleepdec – Continuous variable of the percent of each hour a mouse spent sleeping converted to a decimal between 0 and 1.
Mean_bout – Integer variable of mean bout length of sleep (seconds) for each hour.
Mean_bout_round – Integer variable of mean bout length of sleep (seconds) for each hour rounded to the nearest whole integer.
Total_min – Integer variable of the number of minutes a mouse spent asleep during each hour.
Total_min_round – Integer variable of the number of minutes a mouse spent asleep during each hour rounded to the nearest whole integer.
REM_percent_sleep – Continuous variable of the percent of each hour a mouse spent in rapid eye movement (REM) sleep.
REM_percent_sleepdec – Continuous variable of the percent of each hour a mouse spent in rapid eye movement (REM) sleep converted to a decimal between 0 and 1.
REM_mean_bout – Integer variable of mean bout length of rapid eye movement (REM) sleep (seconds) for each hour.
REM_mean_bout_round – Integer variable of mean bout length of rapid eye movement (REM) sleep (seconds) for each hour rounded to the nearest whole integer.
REM_total_min – Integer variable of the number of minutes a mouse spent in rapid eye movement (REM) sleep during each hour.
REM_total_min_round – Integer variable of the number of minutes a mouse spent in rapid eye movement (REM) sleep during each hour rounded to the nearest whole integer.
nREM_percent_sleep – Continuous variable of the percent of each hour a mouse spent in non-rapid eye movement (nREM) sleep.
nREM_percent_sleepdec – Continuous variable of the percent of each hour a mouse spent in non-rapid eye movement (nREM) sleep converted to a decimal between 0 and 1.
nREM_mean_bout – Integer variable of mean bout length of non-rapid eye movement (nREM) sleep (seconds) for each hour.
nREM_mean_bout_round – Integer variable of mean bout length of non-rapid eye movement (nREM) sleep (seconds) for each hour rounded to the nearest whole integer.
nREM_total_min – Integer variable of the number of minutes a mouse spent in non-rapid eye movement (nREM) sleep during each hour.
nREM_total_min_round – Integer variable of the number of minutes a mouse spent in non-rapid eye movement (nREM) sleep during each hour rounded to the nearest whole integer.
WAKE_percent– Continuous variable of the percent of each hour a mouse spent awake.
WAKE_percent_dec – Continuous variable of the percent of each hour a mouse spent awake converted to a decimal between 0 and 1.
WAKE_mean_bout – Integer variable of mean bout length of wake episode (seconds) for each hour.
WAKE_mean_bout_round – Integer variable of mean bout length of wake episode (seconds) for each hour rounded to the nearest whole integer.
WAKE_total_min – Integer variable of the number of minutes a mouse spent awake during each hour.
WAKE_total_min_round – Integer variable of the number of minutes a mouse spent awake during each hour rounded to the nearest whole integer.
Code/Software
We fit all statistical models in the frequentist framework using the package ‘glmmTMB’ in the R statistical computing environment. Because outcome measures of interest were either percentages (e.g., percent recording time) or overdispersed counts (e.g., bout lengths and total minutes slept), we specified Beta or negative-binomial error distributions, respectively, in the models. We included three-knot and five-knot basis splines for all sleep time and sleep architecture models, respectively, to accommodate both the expected similarity among observations within a period and the nonlinear effects of time. To determine the appropriate number of spline knots for each sleep time and sleep bout length model, we fit models with two to seven spline knots, conducted model selection using Akaike’s Information Criterion corrected for small sample size (AIC*c*), and produced estimates from the top-ranked, most parsimonious model that best described the data for each outcome.
Methods
Adult (8-20 weeks-old) male and female C57BL/6 mice from 17 total sex-specific cohorts were used (males n = 140; females n = 127). All mice were bred in-house from breeder pairs obtained from Jackson Laboratories (Bar Harbor, ME). For all studies, mice were singly housed and maintained on a 12-hour light:dark cycle (200 lux, cool white, fluorescent light) at an ambient temperature of 24°C ± 2°C. All mice were acclimated to non-invasive piezoelectric sleep cages for a minimum of 5 days prior to initiation of data collection and were fed a normal diet of standard rodent chow, and food and water were available ab libitum. Physiological parameters were recorded for 48-hours using non-invasive piezoelectric cages to determine total sleep, non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wakefulness (WAKE).The non-invasive piezoelectric cage system used Polyvinylidine Difluoride sensors on the cage floor to measure movement and breathing patterns. The piezoelectric signals were analyzed over ten-second windows (epochs) at a two-second interval. Data collected from the sleep cages were binned at each hour using a rolling average of the percentage of recording time spent in sleep. Data were also binned by length of individual bout to calculate the hourly mean bout length (duration in seconds). To be considered a bout, a minimum of two consecutive epochs had to be scored as sleep (or wake). Total minutes slept within each experimental period (e.g., day 1) was also calculated. Total sleep was determined using Sleep Stats Version 2.18 to compute a decision statistic that classified epochs using a linear discriminate classier (‘sleep’ or ‘wake’). In addition to total sleep based on a two-state algorithm, a decision tree classifier was used to classify WAKE, NREM, and REM using Sleep Stats Data Explorer Version 4.