DOI: 10.5061/dryad.2280gb64x
title: Cortical astrocyte histamine-1-receptors regulate intracellular calcium and extracellular adenosine dynamics across sleep and wake

description: 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. 
description: # **Cortical astrocyte histamine-1-receptors regulate intracellular
 calcium and extracellular adenosine dynamics across sleep and wake**
 Dataset DOI: [10.5061/dryad.2280gb64x](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)