Astrocytes in mammalian cortex express particularly high levels of the wake-promoting histamine-1-receptor (H1R), yet little is known about how astrocytic H1R contributes to arousal regulation. To address this gap, we test how astrocyte-specific H1R signaling in murine cortex affects local astrocyte calcium (Ca²⁺), sleep/wake dynamics, and extracellular adenosine—an astrocytic output that regulates cortical arousal. Using ex vivo two-photon Ca²⁺imaging in acute cortical slices, we show that H1R mediates cell-autonomous astrocyte Ca²⁺ responses to histamine and attenuates responses to norepinephrine. Next, using freely moving fiber photometry and electroencephalogram/electromyogram recordings, we show that astrocyte-specific H1R deletion in cortex promotes wakefulness, reduces REM sleep, and alters astrocyte Ca²⁺ signals during wake and extracellular adenosine dynamics around REM transitions. Our data indicate that H1R activity not only mediates histamine responses in astrocytes but also modulates their responses to non-histaminergic inputs, potentially through lasting changes in astrocyte physiology that influence adenosine release and REM sleep.

Dataset DOI: 10.5061/dryad.2280gb64x

Overview

This data package contains ex vivo slice imaging data and in vivo photometry/electrophysiology data supporting the manuscript titled above. Ex vivo data support Figs. 1–5 and S1–S3; in vivo data support Figs. 6–8 and S4–S7. File and folder names indicate the main figure(s) each dataset supports. Code used to generate all figures is archived in the accompanying Zenodo repository (DOI: 10.5281/zenodo.16809849).

Ex vivo slice data

Data shown in figures 1–2, 4 and S1, S3:

Fig1_DoseResponse_data.mat

Fig1.zip

Fig2_H1Rpharm_data.mat

Fig2.zip

Fig4_H1RKO_HA_data.mat

Fig4_H1RKO_NE_data.mat

Fig4_H1RKO_NE_postHA_data.mat

Fig4.zip

Datasets are named by figure and experiment. Each dataset includes:

1. .mat file containing:

a. Cell array storing AQuA analysis. Each row corresponds to AQuA output for a single slice, with the slice ID stored in the following format: Fig1_DoseResponse_data.AQuA(i).res.opts.fileName

b. CSV table listing experiment conditions for each slice (includes slice ID, imaging parameters, HA concentration, time of HA addition, etc.)

c. Additional instructions for navigating contents of the .mat file

2. .zip file containing:

a. Minimally processed two-photon tiffs with file names corresponding to slice ID listed in CSV tables and AQuA cell arrays

Additional items for Fig4.zip file:

b. ROI lists. Note that AQuA assigns unique IDs to ROIs used for ROI-based event detection. To indicate group identity, the accompanying CSV table includes two columns ('CreROI' and 'WTROI') that specify which ROIs correspond to the KO and WT groups, respectively.

c. Z-stacks showing Cre-RFP+ or Cre-GFP+ astrocytes

Data shown in figure 5:

Fig5_H1RKO_NE_postLowHA.mat

Fig5_H1RKO_NE.mat

Fig5.zip

Datasets are subdivided by ligand condition: NE and NE post-low HA. Each dataset includes:

1. .mat file containing:

a. Struct of tables (loaded from .csv files). Each table contains ROI-wise fluorescence traces for a single two-photon video.

b. CSV table listing time of ligand application to the slice

c. Additional instructions for navigating contents of the .mat file

2. .zip file containing:

a. Minimally processed two-photon tiffs with file names corresponding to slice ID listed in CSV tables

b. ROI lists

c. Z-stacks showing Cre-RFP+ or Cre-GFP+ astrocytes

Data shown in figure 3 and S2:

Fig3_CreRFP_NeuN_colocalization.csv

Fig3_H1R_mRNA_quantification.csv

Fig3.zip

Datasets are named by experiment. Each dataset includes:

1. .zip file containing folders named according to experiment date (listed in corresponding CSV file) containing:

a. Confocal images

b. ROI lists

2. .csv file containing either:*

a. Quantification of RNAscope puncta or NeuN/Cre-RFP colocalization

b. ROI IDs that correspond to ROI .zip files to be viewed in ImageJ

In vivo photometry data

Data shown in figures 6, 7, S4–S7:

Fig6_KO_jRGECO.pkl

Fig6_WT_grabHA.pkl

Fig6_Fig7_KO_grabAd.pkl

Fig6_Fig7_WT_grabAd.pkl

Fig6_Fig7_photometry_IHC_histology.zip

Datasets are named by figure and experimental cohort. Some mice co-expressed a GRAB sensor with jRGECO and thus contribute to multiple figures.

1. Primary file: a .pkl pandas DataFrame with fields:

a. ID (str): Animal ID.

b. Condition (str): Genotype label (e.g., WT/KO).

c. Rec ID (list[int]): Recording date [YYYY, M, D].

d. Rec# (int): Session number for that mouse/date.

e. FFT (list[list[float]]): Power spectral density values.

f. FFT freqs (list[float]): Frequency bins (Hz).

g. EEG V1 Time (s) (list[float]): Time vector (seconds) for EEG/feature alignment.

h. EEG V1 (mV) (list[float]): EEG values.

i. EMG Time (s) (list[float]): EMG time vector (same grid as EEG).

j. EMG (mV) (list[float]): EMG values.

k. GRAB signal (list[float], cohort-specific): z-scored signal

l. jRGECO signal (list[float], cohort-specific): z-scored signal

m. States (list[float or int]): HMM scores (1=wake; 0=NREM)

n. NREM Bouts, REM Bouts, WAKE Bouts (list[tuple[int,int]]): (start_idx, end_idx) on the EEG V1 Time (s) index grid.

o. WAKE Bouts with Movement Artifacts (list[tuple[int,int]]): Subset of WAKE bouts flagged for motion.

p. Theta/Delta (list[float]): theta/delta power ratio over time.

q. Delta Power (list[float]): Band-limited power over time.

r. EMG_Gamma (list[float]): EMG gamma power over time.

s. EMG Used for REM (int 0/1): Whether EMG_Gamma was used in manual REM scoring for that session.

t. original index (int): Row index in the source table.

2. .zip file (Fig6_Fig7_photometry_IHC_histology.zip) containing:

a. Confocal images of cortical sections showing photometry and EEG screw tracks with IHC labeling for jRGECO and GRAB sensors and/or astrocytic Cre-GFP

In vivo ipsilateral data

Data shown in figure 8 and S5:

Fig8_ipsi_KO.pkl

Fig8_ipsi_SHAM.pkl

Fig8_ipsi_IHC_histology.zip

Cohorts had an EEG screw in the cortex ipsilateral to Cre or sham virus expression. Filenames include “ipsi”.

1. .pkl file with the same schema as above except no “GRAB signal” or “jRGECO signal”.

2. .zip file (Fig8_ipsi_IHC_histology.zip) containing:

a. Confocal images of EEG screw tracks and IHC for astrocytic Cre-GFP or GFP (sham).

Pre-processed in vivo data

The file named “needScoring_photomProcessing.zip” includes all in vivo data in a pre-processed state. In this case, pre-processed refers to incomplete sleep/wake scoring and photometry signal processing. Scoring for wake/NREM/REM and photometry signal processing can be done using the script named “stateScoring_photomProcessing_script_Zenodo.ipynb”, which is archived on Zenodo (DOI: 10.5281/zenodo.16809849). The .zip file contains:

1. Photometry datasets (with corresponding processed data file name):

a. 07172024_CTphotomHA_th_MT_ST_records.pickle (Fig6_WT_grabHA)

b. 07182024_CTphotom_H1RKOCa_th_MT_ST_records.pickle (Fig6_KO_jRGECO)

c. 07182024_CTphotom_H1RADKO_th_MT_ST_records.pickle (Fig6_Fig7_KO_grabAd)

d. 07182024_CTphotom_H1RADsham_th_MT_ST_records.pickle (Fig6_Fig7_WT_grabAd)

2. Ipsilateral datasets

a. 06052024_CT_ThMtSt_edit_sham.pickle

b. 07022024_CT_th_MT_ST_recordsnew_sham.pickle

(Combine two datasets above to generate Fig8_ipsi_SHAM

c. 06052024_CT_ThMtSt_edit_KO.pickle

d. 07022024_CT_th_MT_ST_recordsnew_KO.pickle

(Combine two datasets above to generate Fig8_ipsi_KO)

Code with Data Map (Zenodo scripts with Dryad inputs)

Main and supplementary figures can be generated using the following Zenodo scripts with Dryad data inputs.

Figures 1 & S1

Zenodo: Fig1_script_Zenodo.m

Dryad: Fig1_DoseResponse_data_.mat

Figure 2

Zenodo: Fig2_script_Zenodo.m

Dryad: Fig2_H1Rpharm_data_.mat

Figures 4 & S3

Zenodo: Fig4_script_Zenodo.m

Dryad (per figure):

Fig 4: Fig4_H1RKO_HA_data_.mat; Fig4_H1RKO_NE_postHA_data_.mat; Fig4_H1RKO_NE_data_.mat

Fig S3: Fig4_H1RKO_HA_data_.mat

Figure 5

Zenodo: Fig5_script_Zenodo.ipynb

Dryad: Fig5_H1RKO_NE.mat; Fig5_H1RKO_NE_postLowHA.mat

Figures 6-7 & S4

Zenodo: Fig6_Fig7_script_Zenodo.ipynb

Dryad (per figure):

Fig 6: Fig6_KO_jRGECO; Fig6_Fig7_KO_grabAd; Fig6_Fig7_WT_grabAd; Fig6_WT_grabHA

Fig 7: Fig6_Fig7_KO_grabAd; Fig6_Fig7_WT_grabAd

Fig S4: Fig6_WT_grabHA

Figure 8 & S6-S7

Zenodo: Fig8_script_Zenodo.ipynb

Dryad (per figure):

Fig 8: Fig8_ipsi_KO; Fig8_ipsi_SHAM

Fig S6-S7: Fig6_KO_jRGECO; Fig6_Fig7_KO_grabAd; Fig6_Fig7_WT_grabAd; Fig6_WT_grabHA

Opening the files

MATLAB .mat: S = load('file.mat'); (see the included notes and CSV for IDs/metadata).

Python .pkl:
import pandas as pd
df = pd.read_pickle('file.pkl')

Software & versions

MATLAB R2024a Update 2 (24.1.0.2470319)
Recommended toolboxes: Image Processing (AQuA utilities), Statistics & Machine Learning, Signal Processing (e.g., findpeaks)

Python 3.12.3
Key packages: numpy==1.26.4, pandas==2.2.2, scipy==1.13.0, matplotlib==3.8.4, jupyter==1.0.0, ipykernel==6.29.3
(Full, pinned environment provided in the Zenodo bundle)

Cortical astrocyte histamine-1-receptors regulate intracellular calcium and extracellular adenosine dynamics across sleep and wake

Data files

Abstract

Ex vivo slice data

In vivo photometry data

In vivo ipsilateral data

Pre-processed in vivo data

Code with Data Map (Zenodo scripts with Dryad inputs)

Cortical astrocyte histamine-1-receptors regulate intracellular calcium and extracellular adenosine dynamics across sleep and wake

Data files

Abstract

README: Cortical astrocyte histamine-1-receptors regulate intracellular calcium and extracellular adenosine dynamics across sleep and wake

Ex vivo slice data

In vivo photometry data

In vivo ipsilateral data

Pre-processed in vivo data

Code with Data Map (Zenodo scripts with Dryad inputs)